
import pandas as pd
import plotly.figure_factory as ff
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from IPython.display import display
from plotly.subplots import make_subplots
pd.set_option('display.float_format', lambda x: '%.2f'%x)
pd.set_option('display.max_colwidth', None)
from prophet import Prophet
import scipy.stats as stats
from statsmodels.graphics.gofplots import qqplot
from statsmodels.stats.multicomp import pairwise_tukeyhsd
import seaborn as sns
import matplotlib.pyplot as plt


#reading the data
drug_df =pd.read_csv("F:/DS2024/Projects/Marketing Strategy Dashboard/data_drug.csv", index_col=0, parse_dates=['month'], dayfirst=True)
display(drug_df.head())


#missing value check
drug_df.isnull().sum()

accID          0
accType        0
accSize        0
accTargets     0
district       0
month          0
sales          0
qty            0
strategy1      0
strategy2      0
strategy3      0
salesVisit1    0
salesVisit2    0
salesVisit3    0
salesVisit4    0
salesVisit5    0
compBrand      0
dtype: int64


#Calculating net income, net income per unit and unit_price
#Net Income = Gross Income - Expenses = Sales - total_expenditures
#unit_price = sales/qty
#net_income_per_unit = net_income/qty
drug_df['total_expenditures'] = drug_df['strategy1'] + drug_df['strategy2']+ drug_df['strategy3'] + drug_df['salesVisit1'] + drug_df['salesVisit2'] + drug_df['salesVisit3'] + drug_df['salesVisit4'] + drug_df['salesVisit5']
drug_df['strategy_expenditures'] =  drug_df['strategy1'] + drug_df['strategy2']+ drug_df['strategy3']
drug_df['salesVisit_expenditures'] = drug_df['total_expenditures'] - drug_df['strategy_expenditures']
drug_df['net_income'] = drug_df['sales'] - drug_df['total_expenditures']
drug_df['net_income_per_unit'] = drug_df['net_income'].divide(drug_df['qty'].abs()).replace(np.inf, 0).replace(-np.inf, 0) #.round(2)
drug_df['unit_price'] = drug_df['sales'].divide(drug_df['qty'].abs()).replace(np.inf, 0).replace(-np.inf, 0) #.round(2)
drug_df = drug_df.fillna(0)
#drug_df = drug_df.round(2)


for column_name in drug_df.columns:
    unique_values = len(drug_df[column_name].unique())
    print("Dataset has '{unique_values}' unique '{column_name}'".format(column_name = column_name, unique_values = unique_values))

Dataset has '124' unique 'accID'
Dataset has '4' unique 'accType'
Dataset has '71' unique 'accSize'
Dataset has '39' unique 'accTargets'
Dataset has '1' unique 'district'
Dataset has '24' unique 'month'
Dataset has '160' unique 'sales'
Dataset has '133' unique 'qty'
Dataset has '219' unique 'strategy1'
Dataset has '754' unique 'strategy2'
Dataset has '29' unique 'strategy3'
Dataset has '27' unique 'salesVisit1'
Dataset has '67' unique 'salesVisit2'
Dataset has '69' unique 'salesVisit3'
Dataset has '16' unique 'salesVisit4'
Dataset has '6' unique 'salesVisit5'
Dataset has '2' unique 'compBrand'
Dataset has '986' unique 'total_expenditures'
Dataset has '886' unique 'strategy_expenditures'
Dataset has '98' unique 'salesVisit_expenditures'
Dataset has '1381' unique 'net_income'
Dataset has '1134' unique 'net_income_per_unit'
Dataset has '163' unique 'unit_price'


drug_df.describe().T


#creating dataset with all the return orders
return_orders = drug_df[drug_df['qty']<0]
display(return_orders)
display(return_orders.accType.value_counts())

Hospital      10
Polyclinic     1
Name: accType, dtype: int64


return_orders_small_agg = return_orders.groupby("compBrand").agg({'qty':'sum', 'sales':'sum', 'net_income':'sum'}).reset_index()
return_orders_small_agg.columns = ['compBrand', 'return_qty', 'sales_loss', 'net_income_loss']

display(return_orders_small_agg)


return_orders_aggregation = return_orders.groupby(['accType', "compBrand"])['accSize',
       'sales', 'qty', 'strategy1', 'strategy2', 'strategy3', 'salesVisit1',
       'salesVisit2', 'salesVisit3', 'salesVisit4', 'salesVisit5',
       'net_income','total_expenditures', 'strategy_expenditures',
       'salesVisit_expenditures'].apply(lambda x : x.sum()).reset_index()
return_orders_aggregation['percentage_profit'] = (return_orders_aggregation['net_income'].divide(return_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
return_orders_aggregation['percentage_strategy1'] = (return_orders_aggregation['strategy1'].divide(return_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
return_orders_aggregation['percentage_strategy2'] = (return_orders_aggregation['strategy2'].divide(return_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
return_orders_aggregation['percentage_strategy3'] = (return_orders_aggregation['strategy3'].divide(return_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
return_orders_aggregation['percentage_salesVisit'] = (return_orders_aggregation['salesVisit_expenditures'].divide(return_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
return_orders_aggregation = return_orders_aggregation.fillna(0)
return_orders_aggregation = return_orders_aggregation.round(2)
return_orders_aggregation['percentage_loss'] = return_orders_aggregation['percentage_profit'].abs()
display(return_orders_aggregation.head(5))

C:\Users\SHUBHAM HARDE\AppData\Local\Temp\ipykernel_8124\3835801246.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  return_orders_aggregation = return_orders.groupby(['accType', "compBrand"])['accSize',


fig = px.sunburst(return_orders_aggregation[return_orders_aggregation['accType'] != 'Polyclinic'], path=['accType', 'compBrand',  'percentage_strategy1', 'percentage_strategy2', 'percentage_strategy3',
                                                   'percentage_salesVisit', 'percentage_loss'], values='percentage_loss', color = 'percentage_loss', 
                  hover_data = ['accType', 'compBrand',  'percentage_strategy1', 'percentage_strategy2', 'percentage_strategy3','percentage_salesVisit', 'percentage_loss'] )
fig.update_layout(title = "Return order analysis", title_font_color="red", title_x=0.475)
fig.write_html("Return order analysis.html")
fig.show()


#creating dataset with all the successful orders
successful_orders = drug_df[(drug_df['qty'] > 0) & (drug_df['sales'] > 0)]
successful_orders_small_agg = successful_orders.groupby("compBrand").agg({"qty": "sum", "sales": "sum", "net_income": "sum"}).reset_index()
successful_orders_small_agg.columns = ['compBrand', 'placed_qty', 'sales','net_income']
display(successful_orders_small_agg)


successful_return_orders_comparison = pd.merge(successful_orders_small_agg, return_orders_small_agg, on='compBrand', how='inner')
successful_return_orders_comparison['precentage_return'] = ((successful_return_orders_comparison['return_qty']/successful_return_orders_comparison['placed_qty'])*100).abs().round(2)
successful_return_orders_comparison['precentage_sale_loss'] = ((successful_return_orders_comparison['sales_loss']/successful_return_orders_comparison['sales'])*100).abs().round(2)
successful_return_orders_comparison['precentage_net_income_loss_on_return'] = ((successful_return_orders_comparison['net_income_loss']/successful_return_orders_comparison['net_income'])*100).abs().round(2)
display(successful_return_orders_comparison)


successful_orders_aggregation = successful_orders.groupby(['accType', "compBrand"])['accSize',
       'sales', 'qty', 'strategy1', 'strategy2', 'strategy3', 'salesVisit1',
       'salesVisit2', 'salesVisit3', 'salesVisit4', 'salesVisit5',
       'net_income','total_expenditures', 'strategy_expenditures',
       'salesVisit_expenditures'].apply(lambda x : x.sum()).reset_index()
successful_orders_aggregation['percentage_profit'] = (successful_orders_aggregation['net_income'].divide(successful_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
successful_orders_aggregation['percentage_strategy1'] = (successful_orders_aggregation['strategy1'].divide(successful_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
successful_orders_aggregation['percentage_strategy2'] = (successful_orders_aggregation['strategy2'].divide(successful_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
successful_orders_aggregation['percentage_strategy3'] = (successful_orders_aggregation['strategy3'].divide(successful_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
successful_orders_aggregation['percentage_salesVisit'] = (successful_orders_aggregation['salesVisit_expenditures'].divide(successful_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
successful_orders_aggregation = successful_orders_aggregation.round(2)
display(successful_orders_aggregation.head(10))

C:\Users\SHUBHAM HARDE\AppData\Local\Temp\ipykernel_8124\1552486922.py:1: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.


fig = px.sunburst(successful_orders_aggregation, path=['accType', 'compBrand',  'percentage_strategy1', 'percentage_strategy2', 'percentage_strategy3',
                                                       'percentage_salesVisit', 'percentage_profit'], values='percentage_profit', color = 'percentage_profit', 
                  hover_data = ['accType', 'compBrand',  'percentage_strategy1', 'percentage_strategy2', 'percentage_strategy3','percentage_salesVisit', 'percentage_profit'] )
fig.update_layout(title = "Placed order analysis", title_font_color="red", title_x=0.475)
fig.write_html("Placed order analysis.html")
fig.show()


#creating dataset with all no orders
no_orders = drug_df[drug_df['qty'] == 0]

no_orders_small_agg = no_orders.groupby("compBrand").agg({"qty": "sum", "sales": "sum", "net_income": "sum"}).reset_index()
no_orders_small_agg.columns = ['compBrand', 'qty', 'sales_loss_zero','net_income_loss_zero']
no_orders_small_agg = no_orders_small_agg[['compBrand','net_income_loss_zero']]
display(no_orders_small_agg)


successful_return_no_orders_comparison = pd.merge(successful_return_orders_comparison, no_orders_small_agg, on='compBrand', how='inner')
successful_return_no_orders_comparison['precentage_net_income_loss'] = (((successful_return_no_orders_comparison['net_income_loss'] + successful_return_no_orders_comparison['net_income_loss_zero'])/successful_return_no_orders_comparison['net_income'])*100).abs().round(2)
display(successful_return_no_orders_comparison[['compBrand', 'placed_qty', 'return_qty', 'precentage_return', 'precentage_sale_loss', 'precentage_net_income_loss_on_return', 'precentage_net_income_loss']])


no_orders_aggregation = no_orders.groupby(['accType', "compBrand"])['accSize',
       'sales', 'qty', 'strategy1', 'strategy2', 'strategy3', 'salesVisit1',
       'salesVisit2', 'salesVisit3', 'salesVisit4', 'salesVisit5',
       'net_income','total_expenditures', 'strategy_expenditures',
       'salesVisit_expenditures'].apply(lambda x : x.sum()).reset_index()
no_orders_aggregation['percentage_profit'] = (no_orders_aggregation['net_income'].divide(no_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
no_orders_aggregation['percentage_strategy1'] = (no_orders_aggregation['strategy1'].divide(no_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
no_orders_aggregation['percentage_strategy2'] = (no_orders_aggregation['strategy2'].divide(no_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
no_orders_aggregation['percentage_strategy3'] = (no_orders_aggregation['strategy3'].divide(no_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
no_orders_aggregation['percentage_salesVisit'] = (no_orders_aggregation['salesVisit_expenditures'].divide(no_orders_aggregation['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
no_orders_aggregation = no_orders_aggregation.round(2)
no_orders_aggregation = no_orders_aggregation.fillna(0)
display(no_orders_aggregation.head(10))

C:\Users\SHUBHAM HARDE\AppData\Local\Temp\ipykernel_8124\2323339326.py:1: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.


fig = px.sunburst(no_orders_aggregation, path=['accType', 'compBrand',  'percentage_strategy1', 'percentage_strategy2', 'percentage_strategy3',
                                               'percentage_salesVisit', 'total_expenditures'], values='total_expenditures', color = 'compBrand', 
                   hover_data = ['accType', 'compBrand',  'percentage_strategy1', 'percentage_strategy2', 'percentage_strategy3','percentage_salesVisit', 'total_expenditures'] )
fig.update_layout(title = "zero order analysis", title_font_color="red", title_x=0.475)
fig.write_html("zero order analysis.html")
fig.show()


successful_return_no_orders_comparison


fig = go.Figure()
accTypes = drug_df['accType'].unique()
for accType in accTypes:
    fig.add_trace(go.Violin(x=drug_df['accType'][drug_df['accType'] == accType],
                            y=drug_df['net_income'][drug_df['accType'] == accType],
                            name=accType,
                            box_visible=True,
                            meanline_visible=True))
    
fig.update_layout(title = "Distribution and density of net_income", title_font_color="red",)
fig.update_xaxes(title = 'accType',title_font_family="Arial")
fig.update_traces(points='all', # show all points
                  jitter=0.05,  # add some jitter on points for better visibility
                  scalemode='count')
fig.write_html("Distribution and density of net_income.html")
fig.show()


import plotly.graph_objects as go
fig = go.Figure()
accTypes = drug_df['accType'].unique()
pointpos_net_income_per_unit = [-0.35,-0.76,-0.55,-0.3]
pointpos_unit_price = [0.6,0.66,0.5,0.35]

show_legend = [True,False,False,False]
for accType, i in zip(accTypes, range(len(accTypes))):
    fig.add_trace(go.Violin(x=successful_orders['accType'][successful_orders['accType'] == accType],
                            y=successful_orders['net_income_per_unit'][successful_orders['accType'] == accType],
                            name = 'net_income_per_unit',
                            line_color='lightseagreen',
                            #box_visible=True,
                            pointpos=pointpos_net_income_per_unit[i],
                            side='negative',
                            showlegend=show_legend[i]))
    
    fig.add_trace(go.Violin(x=successful_orders['accType'][successful_orders['accType'] == accType],
                            y=successful_orders['unit_price'][successful_orders['accType'] == accType],
                            name = 'unit_price',
                            line_color='mediumpurple',
                            #box_visible=True,
                            pointpos=pointpos_unit_price[i],
                            side='positive',
                            showlegend=show_legend[i]))
    
fig.update_layout(title = "Distribution and density of net_income_per_unit vs unit_price for successfull orders", title_font_color="red",
                  violingap=0, violingroupgap=0, violinmode='overlay')
fig.update_xaxes(title = 'accType',title_font_family="Arial")
fig.update_traces(meanline_visible=True,
                  points='all', # show all points
                  jitter=0.05,  # add some jitter on points for better visibility
                  scalemode='count'
                  ) 
fig.write_html("Distribution and density of net_income_per_unit vs unit_price.html")
fig.show()


#adding quaters to analyse strategies performace in a quater
drug_df['quarter'] = pd.PeriodIndex(drug_df.month, freq='Q')


def aggregation_func(drug_df, list_of_strategies, include_salesVisit = False, quarter = False):
  """
  drug_df : pandas data frame
  list_of_strategies : strategies in the form of list e.g., ['strategy1] or ['strategy1', 'strategy2'] or [] for no strategy
  include_salesVisit: default "False", if "True" then include salesVisit expenditures
  quarter: default "False", if "True" then do the quarter wise aggregation

  This function will do the strategy wise aggregation
  """
  if isinstance(list_of_strategies, list):
    if(all(x in ['strategy1', 'strategy2', 'strategy3'] for x in list_of_strategies)):
      n_strategies = len(list_of_strategies)
      if (n_strategies == 0  and include_salesVisit == False):
        print("drug_df[(drug_df['total_expenditures'] == 0)]")
        temp_df = drug_df[(drug_df['total_expenditures'] == 0)]
      elif (n_strategies == 0  and include_salesVisit == True):
        print("drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]")
        temp_df = drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]
      else:
        temp_sing = n_strategies
        if include_salesVisit:
          expenditures_addition_str = "drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == "
        else:
          expenditures_addition_str = "drug_df[(drug_df['total_expenditures'] == "
        expenditures_and_str = ""
        for strategy in list_of_strategies:
          expenditures_addition_str = expenditures_addition_str + "drug_df['"+strategy+"']"
          expenditures_and_str = expenditures_and_str + " & (drug_df['"+strategy+"'] > 0)"
          temp_sing = temp_sing - 1
          if temp_sing > 0:
            expenditures_addition_str = expenditures_addition_str + " + "
          else:
            expenditures_str = expenditures_addition_str + ")"
            expenditures_str = expenditures_str + expenditures_and_str + "]"
        print(expenditures_str)
        temp_df = eval(expenditures_str)
      if quarter:
        print('quarter aggregation')
        temp_df_agg = temp_df.groupby(["accType", "compBrand", "quarter"]).agg({"qty": "sum", "sales": "sum", "net_income": "sum", 
                                                                  "strategy1": "sum", "strategy2": "sum", "strategy3": "sum",
                                                                  "total_expenditures": "sum", "strategy_expenditures": "sum","salesVisit_expenditures": "sum"}
                                                                  ).reset_index()
      else:
        print('without quarter aggregation')
        temp_df_agg = temp_df.groupby(["accType", "compBrand"]).agg({"qty": "sum", "sales": "sum", "net_income": "sum", 
                                                                    "strategy1": "sum", "strategy2": "sum", "strategy3": "sum",
                                                                    "total_expenditures": "sum", "strategy_expenditures": "sum","salesVisit_expenditures": "sum"}
                                                                    ).reset_index()
      temp_df_agg['unit_price'] = temp_df_agg['sales']/temp_df_agg['qty']
      temp_df_agg['net_income_per_unit'] = temp_df_agg['net_income']/temp_df_agg['qty']
      temp_df_agg['expenditures_per_unit'] = temp_df_agg['unit_price'] - temp_df_agg['net_income_per_unit']
      temp_df_agg['percentage_profit'] = (temp_df_agg['net_income'].divide(temp_df_agg['total_expenditures'].abs()).replace(np.inf, 0).round(4))*100
      temp_df_agg['percentage_strategy1'] = (temp_df_agg['strategy1']/temp_df_agg['total_expenditures'])*100
      temp_df_agg['percentage_strategy2'] = (temp_df_agg['strategy2']/temp_df_agg['total_expenditures'])*100
      temp_df_agg['percentage_strategy3'] = (temp_df_agg['strategy3']/temp_df_agg['total_expenditures'])*100
      temp_df_agg['percentage_salesVisit'] = (temp_df_agg['salesVisit_expenditures']/temp_df_agg['total_expenditures'])*100

      return temp_df_agg
    else:
      raise KeyError(", ".join(list_of_strategies) + " is not in ['strategy1', 'strategy2', 'strategy3']")
  else:
    raise TypeError("Please pass strategies in the form of list e.g., ['strategy1] or ['strategy1', 'strategy2'] or [] for no strategy")


imp_cols_for_analysis = ['accType', 'compBrand', 'sales', 'net_income', 'unit_price', 'net_income_per_unit','expenditures_per_unit',
       'percentage_strategy1','percentage_strategy2', 'percentage_strategy3','percentage_salesVisit']


#this list will store the data with all types of strategies
List_of_all_types_of_data = []


data_with_zero_expenditures = aggregation_func(drug_df, [], False)
List_of_all_types_of_data.append('data_with_zero_expenditures')
display(data_with_zero_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == 0)]
without quarter aggregation


data_only_with_salesVisit_expenditures = aggregation_func(drug_df, [], True)
List_of_all_types_of_data.append('data_only_with_salesVisit_expenditures')
display(data_only_with_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]
without quarter aggregation


data_only_with_strategy1_expenditures = aggregation_func(drug_df, ['strategy1'], False)
List_of_all_types_of_data.append('data_only_with_strategy1_expenditures')
display(data_only_with_strategy1_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy1']) & (drug_df['strategy1'] > 0)]
without quarter aggregation


data_with_strategy1_and_salesVisit_expenditures = aggregation_func(drug_df, ['strategy1'], True)
List_of_all_types_of_data.append('data_with_strategy1_and_salesVisit_expenditures')
display(data_with_strategy1_and_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1']) & (drug_df['strategy1'] > 0)]
without quarter aggregation


data_only_with_strategy2_expenditures = aggregation_func(drug_df, ['strategy2'], False)
List_of_all_types_of_data.append('data_only_with_strategy2_expenditures')
display(data_only_with_strategy2_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy2']) & (drug_df['strategy2'] > 0)]
without quarter aggregation


data_with_strategy2_and_salesVisit_expenditures = aggregation_func(drug_df, ['strategy2'], True)
List_of_all_types_of_data.append('data_with_strategy2_and_salesVisit_expenditures')
display(data_with_strategy2_and_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy2']) & (drug_df['strategy2'] > 0)]
without quarter aggregation


data_only_with_strategy3_expenditures = aggregation_func(drug_df, ['strategy3'], False)
List_of_all_types_of_data.append('data_only_with_strategy3_expenditures')
display(data_only_with_strategy3_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy3']) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_with_strategy3_and_salesVisit_expenditures = aggregation_func(drug_df, ['strategy3'], True)
List_of_all_types_of_data.append('data_with_strategy3_and_salesVisit_expenditures')
display(data_with_strategy3_and_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy3']) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_only_with_strategy1_and_strategy2_expenditures = aggregation_func(drug_df, ['strategy1', 'strategy2'], False)
List_of_all_types_of_data.append('data_only_with_strategy1_and_strategy2_expenditures')
display(data_only_with_strategy1_and_strategy2_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy1'] + drug_df['strategy2']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0)]
without quarter aggregation


data_with_strategy1_strategy2_and_salesVisit_expenditures = aggregation_func(drug_df, ['strategy1', 'strategy2'], True)
List_of_all_types_of_data.append('data_with_strategy1_strategy2_and_salesVisit_expenditures')
display(data_with_strategy1_strategy2_and_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy2']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0)]
without quarter aggregation


data_only_with_strategy1_and_strategy3_expenditures = aggregation_func(drug_df, ['strategy1', 'strategy3'], False)
List_of_all_types_of_data.append('data_only_with_strategy1_and_strategy3_expenditures')
display(data_only_with_strategy1_and_strategy3_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy1'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_with_strategy1_strategy3_and_salesVisit_expenditures = aggregation_func(drug_df, ['strategy1', 'strategy3'], True)
List_of_all_types_of_data.append('data_with_strategy1_strategy3_and_salesVisit_expenditures')
display(data_with_strategy1_strategy3_and_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_only_with_strategy2_and_strategy3_expenditures = aggregation_func(drug_df, ['strategy2', 'strategy3'], False)
List_of_all_types_of_data.append('data_only_with_strategy2_and_strategy3_expenditures')
display(data_only_with_strategy2_and_strategy3_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_with_strategy2_strategy3_and_salesVisit_expenditures = aggregation_func(drug_df, ['strategy2', 'strategy3'], True)
List_of_all_types_of_data.append('data_with_strategy2_strategy3_and_salesVisit_expenditures')
display(data_with_strategy2_strategy3_and_salesVisit_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_only_with_strategies_expenditures = aggregation_func(drug_df, ['strategy1', 'strategy2', 'strategy3'], False)
List_of_all_types_of_data.append('data_only_with_strategies_expenditures')
display(data_only_with_strategies_expenditures[imp_cols_for_analysis])

drug_df[(drug_df['total_expenditures'] == drug_df['strategy1'] + drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
without quarter aggregation


data_with_all_type_of_expenditures = aggregation_func(drug_df, ['strategy1', 'strategy2', 'strategy3'], True)
List_of_all_types_of_data.append('data_with_all_type_of_expenditures')
display(data_with_all_type_of_expenditures[imp_cols_for_analysis])

drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
without quarter aggregation


#combining all the strategies to single dataframe called over_all_data_comparison
over_all_data_comparison = pd.DataFrame(columns = ['type_of_agg','accType', 'compBrand', 'qty', 'sales', 'net_income', 'strategy1',
       'strategy2', 'strategy3', 'total_expenditures', 'strategy_expenditures',
       'salesVisit_expenditures', 'unit_price', 'net_income_per_unit',
       'expenditures_per_unit', 'percentage_profit',
       'percentage_strategy1', 'percentage_strategy2',
       'percentage_strategy3', 'percentage_salesVisit'] )


for agg_data in List_of_all_types_of_data:
  exec(agg_data+"['type_of_agg']" + "=" + "agg_data")
  over_all_data_comparison = pd.concat([over_all_data_comparison, eval(agg_data)], ignore_index=True)

over_all_data_comparison = over_all_data_comparison.sort_values(by=['accType', "compBrand"], ignore_index=True)
over_all_data_comparison = over_all_data_comparison.round(2)
display(over_all_data_comparison.head(100))


print(List_of_all_types_of_data)

['data_with_zero_expenditures', 'data_only_with_salesVisit_expenditures', 'data_only_with_strategy1_expenditures', 'data_with_strategy1_and_salesVisit_expenditures', 'data_only_with_strategy2_expenditures', 'data_with_strategy2_and_salesVisit_expenditures', 'data_only_with_strategy3_expenditures', 'data_with_strategy3_and_salesVisit_expenditures', 'data_only_with_strategy1_and_strategy2_expenditures', 'data_with_strategy1_strategy2_and_salesVisit_expenditures', 'data_only_with_strategy1_and_strategy3_expenditures', 'data_with_strategy1_strategy3_and_salesVisit_expenditures', 'data_only_with_strategy2_and_strategy3_expenditures', 'data_with_strategy2_strategy3_and_salesVisit_expenditures', 'data_only_with_strategies_expenditures', 'data_with_all_type_of_expenditures']


#handing the null and infinity values after the aggregation for plotting
over_all_data_comparison_exp = over_all_data_comparison.fillna(0).replace(-np.inf, 0).replace(np.inf, 0)


labels = ['Sales_4', 'net_income_4', 'Sales_5', 'net_income_5']
colors = ['RGB(227,207,87)', 'RGB(0,0,205)', 'RGB(255,97,3)', 'RGB(5,5,5)']

line_size = [2, 4, 2, 4]

for accType_ in over_all_data_comparison_exp['accType'].unique():
  temp_over_all_data_comparison_exp = over_all_data_comparison_exp[over_all_data_comparison_exp['accType'] == accType_]
  x_data = [list(temp_over_all_data_comparison_exp['type_of_agg'].unique()),
            list(temp_over_all_data_comparison_exp['type_of_agg'].unique()),
            list(temp_over_all_data_comparison_exp['type_of_agg'].unique()),
            list(temp_over_all_data_comparison_exp['type_of_agg'].unique())]
  y_data = [list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['sales']),
            list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['net_income']),
            list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['sales']),
            list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['net_income'])]

  text_data = [list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['qty']),
        list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['qty']),
        list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['qty']),
        list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['qty'])]

  fig = go.Figure()

  for i in range(len(y_data)):
    if (i % 2) == 0:
      cat_val = 'sales'
    else:
      cat_val = 'net_income'
    fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines',
        name=labels[i],
        line=dict(color=colors[i], width=line_size[i]),
        text = text_data[i],
        connectgaps=True,
        hovertemplate= '<b>type_of_agg</b>: %{x}<br>'+
                       '<b>'+cat_val+'<b>: %{y}<br>' +
                       '<b>qty</b>: %{text}<br>',
    ))

  fig.update_layout(title = "sales vs net_income from "+accType_, title_font_color="red", yaxis=dict(tickformat="20,.2f"))
  fig.write_html("sales vs net_income from "+accType_+".html")
  fig.show()


labels = ['unit_price_4', 'net_income_per_unit_4', 'unit_price_5', 'net_income_per_unit_5']
colors = ['RGB(227,207,87)', 'RGB(0,0,205)', 'RGB(255,97,3)', 'RGB(5,5,5)']

line_size = [2, 4, 2, 4]

for accType_ in over_all_data_comparison_exp['accType'].unique():
  temp_over_all_data_comparison_exp = over_all_data_comparison_exp[over_all_data_comparison_exp['accType'] == accType_]
  x_data = [list(temp_over_all_data_comparison_exp['type_of_agg'].unique()),
            list(temp_over_all_data_comparison_exp['type_of_agg'].unique()),
            list(temp_over_all_data_comparison_exp['type_of_agg'].unique()),
            list(temp_over_all_data_comparison_exp['type_of_agg'].unique())]
  y_data = [list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['unit_price']),
            list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['net_income_per_unit']),
            list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['unit_price']),
            list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['net_income_per_unit'])]

  text_data = [list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['qty']),
        list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 4]['qty']),
        list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['qty']),
        list(temp_over_all_data_comparison_exp[temp_over_all_data_comparison_exp['compBrand'] == 5]['qty'])]


  fig = go.Figure()

  for i in range(len(y_data)):
      fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines',
          name=labels[i],
          line=dict(color=colors[i], width=line_size[i]),
          text = text_data[i],
          connectgaps=True,
          hovertemplate= '<b>type_of_agg</b>: %{x}<br>'+
                       '<b>'+cat_val+'<b>: %{y}<br>' +
                       '<b>qty</b>: %{text}<br>',
      ))

  fig.update_layout(title = "unit_price vs net_income_per_unit from "+accType_, title_font_color="red",yaxis=dict(tickformat="20,.2f"))
  fig.write_html("unit_price vs net_income_per_unit from "+accType_+".html")
  fig.show()


fig = px.scatter_3d(over_all_data_comparison, x='type_of_agg', y='net_income', z='qty', color='accType', symbol = 'compBrand', width=900, height=900, hover_data= over_all_data_comparison)
fig.update_layout(title = "type_of_agg vs net_income vs qty for each accType and compBrand", title_font_color="red", yaxis=dict(tickformat="20,.2f"))
fig.write_html("type_of_agg vs net_income vs qty for each accType and compBrand.html")
fig.show()


index_of_best_results = over_all_data_comparison.groupby(["accType", "compBrand"])['net_income'].transform(max) == over_all_data_comparison['net_income']
data_with_best_result = over_all_data_comparison[index_of_best_results]
imp_cols_for_analysis_best_result = ['type_of_agg']
imp_cols_for_analysis_best_result.extend(imp_cols_for_analysis)
display(data_with_best_result[imp_cols_for_analysis_best_result])


index_of_worst_results = over_all_data_comparison.groupby(["accType", "compBrand"])['net_income'].transform(min) == over_all_data_comparison['net_income']
data_with_worst_result = over_all_data_comparison[index_of_worst_results]
display(data_with_worst_result[imp_cols_for_analysis_best_result])


#data_for_corr = data.copy()
data_for_corr = pd.get_dummies(drug_df, columns=["accType"])
data_for_corr = data_for_corr.replace(np.nan, 0).replace(-np.inf, 0).replace(np.inf, 0)
plt.figure(figsize = (18,10))
ax = sns.heatmap(data_for_corr.corr(), annot = True, fmt='.2g',cmap= 'coolwarm',linewidths=1)


full_time_series_data = pd.DataFrame(columns=['ds', 'trend', 'yhat_lower', 'yhat_upper', 'trend_lower', 'trend_upper',
                                              'additive_terms', 'additive_terms_lower', 'additive_terms_upper',
                                              'extra_regressors_additive', 'extra_regressors_additive_lower',
                                              'extra_regressors_additive_upper', 'salesVisit1', 'salesVisit1_lower',
                                              'salesVisit1_upper', 'salesVisit2', 'salesVisit2_lower',
                                              'salesVisit2_upper', 'salesVisit3', 'salesVisit3_lower',
                                              'salesVisit3_upper', 'salesVisit4', 'salesVisit4_lower',
                                              'salesVisit4_upper', 'salesVisit5', 'salesVisit5_lower',
                                              'salesVisit5_upper', 'strategy1', 'strategy1_lower', 'strategy1_upper',
                                              'strategy2', 'strategy2_lower', 'strategy2_upper', 'strategy3',
                                              'strategy3_lower', 'strategy3_upper', 'multiplicative_terms',
                                              'multiplicative_terms_lower', 'multiplicative_terms_upper', 'yhat', 'accID'])

for i in drug_df['accID'].unique():
    data_time_series_all = drug_df[(drug_df['accID'] == i)]
    data_time_series_all = data_time_series_all.sort_values(by=['month'])
    data_time_series_all = data_time_series_all.reset_index()
    data_time_series = data_time_series_all[(data_time_series_all['compBrand'] == 4)]
    data_time_series = data_time_series[['month', 'sales', 'strategy1', 'strategy2', 'strategy3',
                                         'salesVisit1', 'salesVisit2', 'salesVisit3', 'salesVisit4', 'salesVisit5']]
    data_time_series.columns = ['ds', 'y', 'strategy1', 'strategy2', 'strategy3',
                                'salesVisit1', 'salesVisit2', 'salesVisit3', 'salesVisit4', 'salesVisit5']

    m = Prophet()
    m.add_regressor('strategy1')
    m.add_regressor('strategy2')
    m.add_regressor('strategy3')
    m.add_regressor('salesVisit1')
    m.add_regressor('salesVisit2')
    m.add_regressor('salesVisit3')
    m.add_regressor('salesVisit4')
    m.add_regressor('salesVisit5')

    m.fit(data_time_series)
    future = m.make_future_dataframe(periods=4, freq='MS')
    future['strategy1'] = data_time_series_all['strategy1']
    future['strategy2'] = data_time_series_all['strategy2']
    future['strategy3'] = data_time_series_all['strategy3']
    future['salesVisit1'] = data_time_series_all['salesVisit1']
    future['salesVisit2'] = data_time_series_all['salesVisit2']
    future['salesVisit3'] = data_time_series_all['salesVisit3']
    future['salesVisit4'] = data_time_series_all['salesVisit4']
    future['salesVisit5'] = data_time_series_all['salesVisit5']

    fcst = m.predict(future)
    print()
    fig = m.plot(fcst, xlabel='Date', ylabel='Bounce rate (in %)', figsize=(16, 8))
    fcst['accID'] = i
    ax = fig.gca()
    ax.set_title("Forcasted result without the new competitor for accID =" + i, size=12)
    ax.set_xticks(future['ds'])
    ax.set_xticklabels((future['ds'].dt.strftime('%m-%y').tolist()))
    full_time_series_data = pd.concat([full_time_series_data, fcst], ignore_index=True)
    print(i)
    print(fcst[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(8))

12:54:01 - cmdstanpy - INFO - Chain [1] start processing
12:54:01 - cmdstanpy - INFO - Chain [1] done processing

XYZ-987002
           ds        yhat  yhat_lower  yhat_upper
16 2015-02-01  7916353.39  5041177.64 10921476.37
17 2015-03-01  7232611.37  4399987.48  9903636.13
18 2015-04-01  7867678.84  5310614.43 10841414.42
19 2015-05-01 10494318.89  7711048.30 13375893.76
20 2015-06-01  8314022.05  5363377.39 11160660.77
21 2015-07-01  8411392.36  5544101.50 11328531.31
22 2015-08-01 10276800.17  7590280.75 13275180.80
23 2015-09-01 10938028.33  8543987.31 13818550.59

12:54:02 - cmdstanpy - INFO - Chain [1] start processing
12:54:02 - cmdstanpy - INFO - Chain [1] done processing

XYZ-987005
           ds        yhat  yhat_lower  yhat_upper
16 2015-02-01 20395625.68 17030466.52 23921637.97
17 2015-03-01 22978467.78 19379103.87 26558047.21
18 2015-04-01 29698881.73 26129207.49 32851799.97
19 2015-05-01 25926978.80 22731948.94 29284390.09
20 2015-06-01 31339664.92 27879231.29 34612031.11
21 2015-07-01 32071210.08 28436454.43 35690973.27
22 2015-08-01 36050685.63 32464111.42 39594569.81
23 2015-09-01 45409323.56 41726589.68 48899753.75

12:54:02 - cmdstanpy - INFO - Chain [1] start processing
12:54:02 - cmdstanpy - INFO - Chain [1] done processing

XYZ-987006
           ds        yhat  yhat_lower  yhat_upper
16 2015-02-01  8428017.96  6013211.72 10971507.92
17 2015-03-01  7969182.12  5551319.84 10404620.01
18 2015-04-01  8285135.92  5868085.76 10730541.38
19 2015-05-01  9550748.53  7096939.29 11948359.96
20 2015-06-01 10488553.31  7857271.25 12837858.96
21 2015-07-01  8076369.15  5641133.76 10552846.71
22 2015-08-01 11844241.74  9369985.55 14298002.66
23 2015-09-01 15596118.25 13081480.29 18077031.94

12:54:03 - cmdstanpy - INFO - Chain [1] start processing
12:54:03 - cmdstanpy - INFO - Chain [1] done processing

XYZ-987007
           ds        yhat  yhat_lower  yhat_upper
16 2015-02-01 32554605.08 27648890.85 37479924.61
17 2015-03-01 29833964.82 24734594.07 34975451.62
18 2015-04-01 45084743.07 40203307.75 49852454.29
19 2015-05-01 40634651.19 35766286.02 45689300.49
20 2015-06-01 34347995.10 29610800.38 39053947.23
21 2015-07-01 53689681.94 48570993.72 58732378.74
22 2015-08-01 45683043.43 40475182.71 50675221.89
23 2015-09-01 49152210.24 44317612.82 54219802.25

12:54:03 - cmdstanpy - INFO - Chain [1] start processing
12:54:04 - cmdstanpy - INFO - Chain [1] done processing

XYZ-987008
           ds        yhat  yhat_lower  yhat_upper
16 2015-02-01 21583385.48 18327807.59 24615410.89
17 2015-03-01 24524498.36 21262879.57 27632508.09
18 2015-04-01 27068943.25 23853354.72 30408329.31
19 2015-05-01 26535812.11 23543168.19 29816675.14
20 2015-06-01 33377151.63 30390446.30 36396809.04
21 2015-07-01 22051092.26 19190094.21 25561704.24
22 2015-08-01 40346946.12 37275604.66 43327189.53
23 2015-09-01 38535420.31 35406793.15 41820077.80


data_with_5_compBrand = drug_df[drug_df['compBrand'] == 5]
display(data_with_5_compBrand.head(2))


time_series_data_when_compBrand_5 = full_time_series_data[full_time_series_data['ds'] >= '2015-06-01']
time_series_data_when_compBrand_5 = time_series_data_when_compBrand_5[['accID', 'ds', 'yhat', 'yhat_lower', 'yhat_upper']]
time_series_data_when_compBrand_5.columns = ['accID', 'month', 'forcasted_sales', 'forcasted_sales_lower', 'forcasted_sales_upper']
display(time_series_data_when_compBrand_5.head(2))


combined_data_when_5_compBrand =  pd.merge(data_with_5_compBrand, time_series_data_when_compBrand_5, how="left", 
                                            on=["accID", 'month'],)


combined_data_when_5_compBrand['qty_abs'] = combined_data_when_5_compBrand['qty'].abs()
combined_data_when_5_compBrand['sales_dff'] = combined_data_when_5_compBrand['forcasted_sales'] - combined_data_when_5_compBrand['sales']
combined_data_when_5_compBrand['precent_sales_dff'] = ((combined_data_when_5_compBrand['sales_dff']/combined_data_when_5_compBrand['sales'])*100).replace(-np.inf, 0).replace(np.inf, 0)


fig = px.scatter(combined_data_when_5_compBrand, x="sales", y="forcasted_sales", color="accType",
                 size='qty_abs', hover_data=["net_income","sales", "forcasted_sales", "sales_dff", "precent_sales_dff", "qty",
                                              'strategy1', 'strategy2', 'strategy3', 'accType'])
fig.update_layout(title = "sales(in the presence of new dug) vs forcasted prices(in the absence of new dug) for accType", title_font_color="red", yaxis=dict(tickformat="20,.2f"))
fig.write_html("sales vs forcasted prices for accType.html")
fig.show()


combined_data_when_5_compBranda_agg = combined_data_when_5_compBrand.groupby('accType').agg({'sales':'sum', 'forcasted_sales': 'sum', 
        'strategy1':'sum', 'strategy2':'sum', 'strategy3':'sum', 'salesVisit_expenditures':'sum','total_expenditures':'sum' }).reset_index()
combined_data_when_5_compBranda_agg['precent_sales_dff'] = ((combined_data_when_5_compBranda_agg['forcasted_sales'] - combined_data_when_5_compBranda_agg['sales'])/(combined_data_when_5_compBranda_agg['sales']))*100
combined_data_when_5_compBranda_agg['percentage_strategy1'] = (combined_data_when_5_compBranda_agg['strategy1']/combined_data_when_5_compBranda_agg['total_expenditures'])*100
combined_data_when_5_compBranda_agg['percentage_strategy2'] = (combined_data_when_5_compBranda_agg['strategy2']/combined_data_when_5_compBranda_agg['total_expenditures'])*100
combined_data_when_5_compBranda_agg['percentage_strategy3'] = (combined_data_when_5_compBranda_agg['strategy3']/combined_data_when_5_compBranda_agg['total_expenditures'])*100
combined_data_when_5_compBranda_agg['percentage_salesVisit'] = (combined_data_when_5_compBranda_agg['salesVisit_expenditures']/combined_data_when_5_compBranda_agg['total_expenditures'])*100
display(combined_data_when_5_compBranda_agg[["accType","sales","forcasted_sales","precent_sales_dff","percentage_strategy1","percentage_strategy2","percentage_strategy3","percentage_salesVisit"]])


over_all_data_comparison_top3 = pd.DataFrame(columns = over_all_data_comparison.columns)
for i in over_all_data_comparison['accType'].unique():
  temp_over_all_data_comparison_top3 = over_all_data_comparison[(over_all_data_comparison['accType'] == i) & (over_all_data_comparison['compBrand'] == 4)]
  temp_over_all_data_comparison_top3 = temp_over_all_data_comparison_top3.nlargest(3, 'net_income')
  over_all_data_comparison_top3 = pd.concat([over_all_data_comparison_top3, temp_over_all_data_comparison_top3], ignore_index = True)
  temp_over_all_data_comparison_top3 = over_all_data_comparison[(over_all_data_comparison['accType'] == i) & (over_all_data_comparison['compBrand'] == 5)]
  temp_over_all_data_comparison_top3 = temp_over_all_data_comparison_top3.nlargest(3, 'net_income')
  over_all_data_comparison_top3 = pd.concat([over_all_data_comparison_top3, temp_over_all_data_comparison_top3], ignore_index = True)
display(over_all_data_comparison_top3.head(50))


#cheking the quarterly performance of the above type_of_agg
List_top3_types_of_data = []
quarterly_data_with_zero_expenditures = aggregation_func(drug_df,[], False, True)
quarterly_data_with_zero_expenditures['type_of_agg'] = 'data_with_zero_expenditures'
List_top3_types_of_data.append('quarterly_data_with_zero_expenditures')

quarterly_data_only_with_salesVisit_expenditures = aggregation_func(drug_df,[], True, True)
quarterly_data_only_with_salesVisit_expenditures['type_of_agg'] = 'data_only_with_salesVisit_expenditures'
List_top3_types_of_data.append('quarterly_data_only_with_salesVisit_expenditures')

quarterly_data_with_strategy1_and_salesVisit_expenditures = aggregation_func(drug_df,['strategy1'], True, True)
quarterly_data_with_strategy1_and_salesVisit_expenditures['type_of_agg'] = 'data_with_strategy1_and_salesVisit_expenditures'
List_top3_types_of_data.append('quarterly_data_with_strategy1_and_salesVisit_expenditures')

quarterly_data_with_strategy2_and_salesVisit_expenditures = aggregation_func(drug_df,['strategy2'], True, True)
quarterly_data_with_strategy2_and_salesVisit_expenditures['type_of_agg'] = 'data_with_strategy2_and_salesVisit_expenditures'
List_top3_types_of_data.append('quarterly_data_with_strategy2_and_salesVisit_expenditures')

quarterly_data_with_strategy1_strategy2_and_salesVisit_expenditures = aggregation_func(drug_df,['strategy1', 'strategy2'], True, True)
quarterly_data_with_strategy1_strategy2_and_salesVisit_expenditures['type_of_agg'] = 'data_with_strategy1_strategy2_and_salesVisit_expenditures'
List_top3_types_of_data.append('quarterly_data_with_strategy1_strategy2_and_salesVisit_expenditures')

drug_df[(drug_df['total_expenditures'] == 0)]
quarter aggregation
drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]
quarter aggregation
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1']) & (drug_df['strategy1'] > 0)]
quarter aggregation
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy2']) & (drug_df['strategy2'] > 0)]
quarter aggregation
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy2']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0)]
quarter aggregation


quarterly_top3_data_comparison = pd.DataFrame(columns = ['type_of_agg','accType','quarter', 'compBrand', 'qty', 'sales', 'net_income', 'strategy1',
       'strategy2', 'strategy3', 'total_expenditures', 'strategy_expenditures',
       'salesVisit_expenditures', 'unit_price', 'net_income_per_unit',
       'expenditures_per_unit', 'percentage_profit',
       'percentage_strategy1', 'percentage_strategy2',
       'percentage_strategy3', 'percentage_salesVisit'])

for agg_data in List_top3_types_of_data:
  quarterly_top3_data_comparison = pd.concat([quarterly_top3_data_comparison, eval(agg_data)], ignore_index=True)
display(quarterly_top3_data_comparison.head(2))


quarterly_top3_data_comparison_exp = quarterly_top3_data_comparison.replace(np.nan, 0).replace(-np.inf, 0).replace(np.inf, 0)


quarterly_top3_data_comparison_exp['qty_abs'] = quarterly_top3_data_comparison_exp['qty'].abs().astype(int)
quarterly_top3_data_comparison_exp['str_quarter'] = quarterly_top3_data_comparison_exp['quarter'].astype(str)
for i in quarterly_top3_data_comparison_exp['type_of_agg'].unique():
  temp_quarterly_top3_data_comparison_exp = quarterly_top3_data_comparison_exp[quarterly_top3_data_comparison_exp['type_of_agg'] == i]
  fig = px.scatter(temp_quarterly_top3_data_comparison_exp, x="str_quarter", y="net_income", color="accType",
                  hover_data=["type_of_agg","net_income","sales", "str_quarter", "accType", "compBrand", "net_income_per_unit","unit_price",
                                                'percentage_strategy1', 'percentage_strategy2','percentage_strategy3', 'percentage_salesVisit',])
  fig.update_layout(title = "quarterly performace of " + i, title_font_color="red", yaxis=dict(tickformat="20,.2f"))
  fig.write_html("quarterly performace of " +i+".html")
  fig.show()


quarterly_top3_data_comparison_exp['type_of_agg'].unique()

array(['data_with_zero_expenditures',
       'data_only_with_salesVisit_expenditures',
       'data_with_strategy1_and_salesVisit_expenditures',
       'data_with_strategy2_and_salesVisit_expenditures',
       'data_with_strategy1_strategy2_and_salesVisit_expenditures'],
      dtype=object)


#Checking for homogeneity of variance test
stats.levene(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['sales'],
             drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['sales'],
             drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['sales'])

LeveneResult(statistic=1.1532506974729801, pvalue=0.3161536710038296)


#Checking for homogeneity of variance test
stats.levene(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['net_income'],
             drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['net_income'],
             drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['net_income'])

LeveneResult(statistic=0.8011556926375238, pvalue=0.4491837306692238)


fig, axs = plt.subplots(2,3, figsize=(16,10))
qqplot(np.array(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['sales']), line='s', ax=axs[0,0])
qqplot(np.array(drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['sales']), line='s', ax=axs[0,1])
qqplot(np.array(drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['sales']), line='s', ax=axs[0,2])

qqplot(np.array(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['unit_price']), line='s', ax=axs[1,0])
qqplot(np.array(drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['unit_price']), line='s', ax=axs[1,1])
qqplot(np.array(drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['unit_price']), line='s', ax=axs[1,2])

fig.suptitle('Normality check', fontsize = 14)
axs[0,0].set_title('Strategy 1 with sales',  fontsize = 11)
axs[0,1].set_title('Strategy 2 with sales',  fontsize = 11)
axs[0,2].set_title('Strategy 3 with sales',  fontsize = 11)
axs[1,0].set_title('Strategy 1 with unit_price',  fontsize = 11)
axs[1,1].set_title('Strategy 2 with unit_price',  fontsize = 11)
axs[1,2].set_title('Strategy 3 with unit_price',  fontsize = 11)
plt.show()


#Normality Test - Shapiro Wilks Test
print(stats.shapiro(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['sales']))
print(stats.shapiro(drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['sales']))
print(stats.shapiro(drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['sales']))

ShapiroResult(statistic=0.8319554328918457, pvalue=1.3452607083852541e-11)
ShapiroResult(statistic=0.8229351043701172, pvalue=1.7980812296303295e-25)
ShapiroResult(statistic=0.779592752456665, pvalue=0.0028222736436873674)


#Anderson Darling Test
print(stats.anderson(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['sales']))
print(stats.anderson(drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['sales']))
print(stats.anderson(drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['sales']))

AndersonResult(statistic=8.101636414257598, critical_values=array([0.561, 0.639, 0.767, 0.894, 1.064]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))
AndersonResult(statistic=32.608512150622346, critical_values=array([0.572, 0.652, 0.782, 0.912, 1.085]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))
AndersonResult(statistic=1.2246606442835102, critical_values=array([0.497, 0.566, 0.68 , 0.793, 0.943]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))


stats.kruskal(drug_df[(drug_df['strategy1'] == drug_df['strategy_expenditures']) & (drug_df['strategy1'] > 0)]['sales'],
              drug_df[(drug_df['strategy2'] == drug_df['strategy_expenditures']) & (drug_df['strategy2'] > 0)]['sales'],
              drug_df[(drug_df['strategy3'] == drug_df['strategy_expenditures']) & (drug_df['strategy3'] > 0)]['sales'])

KruskalResult(statistic=1.9917393456510615, pvalue=0.3694020458874732)


def data_selection_func(drug_df, list_of_strategies, include_salesVisit = False, quarter = False):
  if isinstance(list_of_strategies, list):
    if(all(x in ['strategy1', 'strategy2', 'strategy3'] for x in list_of_strategies)):
      n_strategies = len(list_of_strategies)
      if (n_strategies == 0  and include_salesVisit == False):
        print("drug_df[(drug_df['total_expenditures'] == 0)]")
        temp_df = drug_df[(drug_df['total_expenditures'] == 0)]
      elif (n_strategies == 0  and include_salesVisit == True):
        print("drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]")
        temp_df = drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]
      else:
        temp_sing = n_strategies
        if include_salesVisit:
          expenditures_addition_str = "drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == "
        else:
          expenditures_addition_str = "drug_df[(drug_df['total_expenditures'] == "
        expenditures_and_str = ""
        for strategy in list_of_strategies:
          expenditures_addition_str = expenditures_addition_str + "drug_df['"+strategy+"']"
          expenditures_and_str = expenditures_and_str + " & (drug_df['"+strategy+"'] > 0)"
          temp_sing = temp_sing - 1
          if temp_sing > 0:
            expenditures_addition_str = expenditures_addition_str + " + "
          else:
            expenditures_str = expenditures_addition_str + ")"
            expenditures_str = expenditures_str + expenditures_and_str + "]"
        print(expenditures_str)
        temp_df = eval(expenditures_str)
      return temp_df
    else:
      raise KeyError(", ".join(list_of_strategies) + " is not in ['strategy1', 'strategy2', 'strategy3']")
  else:
    raise TypeError("Please pass strategies in side list e.g., ['strategy1] or ['strategy1', 'strategy2'] or [] for no strategy")


data_with_zero_expenditures = data_selection_func(drug_df, [], False)
data_with_zero_expenditures['strategy_type'] = 'data_with_zero_expenditures'

data_only_with_salesVisit_expenditures = data_selection_func(drug_df, [], True)
data_only_with_salesVisit_expenditures['strategy_type'] = 'data_only_with_salesVisit_expenditures'

data_only_with_strategy1_expenditures = data_selection_func(drug_df, ['strategy1'], False)
data_only_with_strategy1_expenditures['strategy_type'] = 'data_only_with_strategy1_expenditures'

data_with_strategy1_and_salesVisit_expenditures = data_selection_func(drug_df, ['strategy1'], True)
data_with_strategy1_and_salesVisit_expenditures['strategy_type'] = 'data_with_strategy1_and_salesVisit_expenditures'

data_only_with_strategy2_expenditures = data_selection_func(drug_df, ['strategy2'], False)
data_only_with_strategy2_expenditures['strategy_type'] = 'data_only_with_strategy2_expenditures'

data_with_strategy2_and_salesVisit_expenditures = data_selection_func(drug_df, ['strategy2'], True)
data_with_strategy2_and_salesVisit_expenditures['strategy_type'] = 'data_with_strategy2_and_salesVisit_expenditures'

data_only_with_strategy3_expenditures = data_selection_func(drug_df, ['strategy3'], False)
data_only_with_strategy3_expenditures['strategy_type'] = 'data_only_with_strategy3_expenditures'

data_with_strategy3_and_salesVisit_expenditures = data_selection_func(drug_df, ['strategy3'], True)
data_with_strategy3_and_salesVisit_expenditures['strategy_type'] = 'data_with_strategy3_and_salesVisit_expenditures'

data_only_with_strategy1_and_strategy2_expenditures = data_selection_func(drug_df, ['strategy1', 'strategy2'], False)
data_only_with_strategy1_and_strategy2_expenditures['strategy_type'] = 'data_only_with_strategy1_and_strategy2_expenditures'

data_with_strategy1_strategy2_and_salesVisit_expenditures = data_selection_func(drug_df, ['strategy1', 'strategy2'], True)
data_with_strategy1_strategy2_and_salesVisit_expenditures['strategy_type'] = 'data_with_strategy1_strategy2_and_salesVisit_expenditures'

data_only_with_strategy1_and_strategy3_expenditures = data_selection_func(drug_df, ['strategy1', 'strategy3'], False)
data_only_with_strategy1_and_strategy3_expenditures['strategy_type'] = 'data_only_with_strategy1_and_strategy3_expenditures'

data_with_strategy1_strategy3_and_salesVisit_expenditures = data_selection_func(drug_df, ['strategy1', 'strategy3'], True)
data_with_strategy1_strategy3_and_salesVisit_expenditures['strategy_type'] = 'data_with_strategy1_strategy3_and_salesVisit_expenditures'

data_only_with_strategy2_and_strategy3_expenditures = data_selection_func(drug_df, ['strategy2', 'strategy3'], False)
data_only_with_strategy2_and_strategy3_expenditures['strategy_type'] = 'data_only_with_strategy2_and_strategy3_expenditures'

data_with_strategy2_strategy3_and_salesVisit_expenditures = data_selection_func(drug_df, ['strategy2', 'strategy3'], True)
data_with_strategy2_strategy3_and_salesVisit_expenditures['strategy_type'] = 'data_with_strategy2_strategy3_and_salesVisit_expenditures'

data_only_with_strategies_expenditures = data_selection_func(drug_df, ['strategy1','strategy2', 'strategy3'], False)
data_only_with_strategies_expenditures['strategy_type'] = 'data_only_with_strategies_expenditures'

data_with_all_type_of_expenditures = data_selection_func(drug_df, ['strategy1','strategy2', 'strategy3'], True)
data_with_all_type_of_expenditures['strategy_type'] = 'data_with_all_type_of_expenditures'

drug_df[(drug_df['total_expenditures'] == 0)]
drug_df[(drug_df['strategy_expenditures'] == 0) & (drug_df['salesVisit_expenditures'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy1']) & (drug_df['strategy1'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1']) & (drug_df['strategy1'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy2']) & (drug_df['strategy2'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy2']) & (drug_df['strategy2'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy3']) & (drug_df['strategy3'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy3']) & (drug_df['strategy3'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy1'] + drug_df['strategy2']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy2']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy1'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy3'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy3'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
drug_df[(drug_df['total_expenditures'] == drug_df['strategy1'] + drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]
drug_df[ (drug_df['salesVisit_expenditures'] > 0) & (drug_df['strategy_expenditures'] == drug_df['strategy1'] + drug_df['strategy2'] + drug_df['strategy3']) & (drug_df['strategy1'] > 0) & (drug_df['strategy2'] > 0) & (drug_df['strategy3'] > 0)]


data_with_all_type_of_strategies = pd.DataFrame(columns = ['accID', 'accType', 'accSize', 'accTargets', 'district', 'month',
       'sales', 'qty', 'strategy1', 'strategy2', 'strategy3', 'salesVisit1',
       'salesVisit2', 'salesVisit3', 'salesVisit4', 'salesVisit5', 'compBrand',
       'total_expenditures', 'strategy_expenditures',
       'salesVisit_expenditures', 'net_income', 'net_income_per_unit',
       'unit_price', 'quarter', 'strategy_type'])

for strategy_type in List_of_all_types_of_data:
  data_with_all_type_of_strategies = pd.concat([data_with_all_type_of_strategies, eval(strategy_type)], ignore_index=True)

data_with_all_type_of_strategies = data_with_all_type_of_strategies.replace(np.nan, 0).replace(-np.inf, 0).replace(np.inf, 0)
data_with_all_type_of_strategies['sales'] = data_with_all_type_of_strategies['sales'].astype(float)


def tukey_test(df, var):
    tukey=pairwise_tukeyhsd(endog=df['sales'],groups=df['strategy_type'], alpha=0.05)
    print(tukey)
    if df['strategy_type'].nunique() > 2:
      tukey.plot_simultaneous()
      title="Comparison between strategy_type in " + var
      plt.title(title)
      plt.xlabel("Sales")
      plt.ylabel("strateges")
      plt.show()


tukey_test(data_with_all_type_of_strategies,"Overall strategies")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                              
==============================================================================================================================================================================
                          group1                                                    group2                             meandiff    p-adj      lower          upper      reject
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures       data_only_with_strategy1_and_strategy2_expenditures  -1483188.6625    1.0 -23036938.3335  20070561.0084  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -1994607.4625 0.9975  -8834291.8489   4845076.9239  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures    461269.7125    1.0  -4958477.8899   5881017.3148  False
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures   4057185.3375 0.9989 -11189537.1228  19303907.7978  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   4126321.6486    0.0   2177088.8255   6075554.4717   True
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  11383974.4486    0.0   9490670.5298  13277278.3674   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   4867502.4328    0.0    3801094.374   5933910.4916   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   3481838.9208 0.7834  -2766725.9706   9730403.8122  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   3509236.3375 0.6821  -2280265.8111    9298738.486  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures  -2675543.7483    0.0  -3662139.8702  -1688947.6263   True
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy1_expenditures      -511418.8    1.0 -23108398.1116  22085560.5116  False
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy2_expenditures    1944458.375    1.0 -20264005.2274  24152921.9774  False
      data_only_with_strategy1_and_strategy2_expenditures                        data_with_all_type_of_expenditures      5540374.0 0.9999  -20847212.803   31927960.803  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   5609510.3111 0.9991 -16015514.5468   27234535.169  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  12867163.1111 0.7061  -8752892.1903  34487218.4125  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures   6350691.0953 0.9973 -15212690.3105  27914072.5012  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   4965027.5833 0.9998 -17460109.0875  27390164.2542  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures      4992425.0 0.9997 -17309156.2586  27294006.2586  False
      data_only_with_strategy1_and_strategy2_expenditures                               data_with_zero_expenditures  -1192355.0858    1.0 -22751936.7935   20367226.622  False
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures    2455877.175 0.9981  -6229340.9113  11141095.2613  False
                    data_only_with_strategy1_expenditures                        data_with_all_type_of_expenditures      6051792.8 0.9858 -10637182.4506  22740768.0506  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures   6120929.1111 0.1613   -940151.3821  13182009.6043  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  13378581.9111    0.0   6332735.6978  20424428.1245   True
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures   6862109.8953 0.0506     -7866.4715  13732086.2622  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   5476446.3833 0.7094  -3748731.7903   14701624.557  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures      5503843.8 0.6579  -3416788.7034  14424476.3034  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures   -680936.2858    1.0  -7538976.8981   6177104.3266  False
                    data_only_with_strategy2_expenditures                        data_with_all_type_of_expenditures    3595915.625 0.9998 -12563115.1777  19754946.4277  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   3665051.9361 0.5973  -2031546.9878     9361650.86  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  10922704.7361    0.0   5245000.0564  16600409.4158   True
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures   4406232.7203 0.2487  -1051693.3372   9864158.7778  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   3020569.2083 0.9844  -5207206.5484  11248344.9651  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures    3047966.625 0.9772  -4836833.0446  10932766.2946  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures  -3136813.4608 0.7457   -8579708.151   2306081.2295  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures     69136.3111    1.0 -15278180.0995  15416452.7218  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   7326789.1111 0.9068  -8013524.1888   22667102.411  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures    810317.0953    1.0 -14450018.3714  16070652.5621  False
                       data_with_all_type_of_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   -575346.4167    1.0 -17030897.9301  15880205.0968  False
                       data_with_all_type_of_expenditures           data_with_strategy3_and_salesVisit_expenditures      -547949.0    1.0 -16834721.1645  15738823.1645  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures  -6732729.0858 0.9432 -21987694.9762   8522236.8047  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures      7257652.8    0.0   4676533.8652   9838771.7348   True
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures    741180.7842 0.9863  -1311817.3586    2794178.927  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   -644482.7278    1.0  -7134639.7697   5845674.3141  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   -617085.3111    1.0  -6666540.6878   5432370.0656  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures  -6801865.3969    0.0  -8814561.8803  -4789168.9134   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -6516472.0158    0.0  -8516445.1418  -4516498.8898   True
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -7902135.5278 0.0041 -14375714.8746   -1428556.181   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -7874738.1111 0.0013 -13906404.6706  -1843071.5516   True
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -14059518.1969    0.0  -16018098.873 -12100937.5207   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   -1385663.512 0.9998  -7667371.5582   4896044.5342  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -1358266.0953 0.9996  -7183523.8496    4466991.659  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -7543046.1811    0.0   -8721462.357  -6364630.0051   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures     27397.4167    1.0  -8448507.5473   8503302.3806  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures  -6157382.6691 0.0594 -12426034.9754    111269.6373  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures  -6184780.0858 0.0259 -11995956.7019   -373603.4696   True
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_all_type_of_strategies[data_with_all_type_of_strategies['compBrand'] == 4],"Overall strategies with 4 compBrand")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
=============================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower          upper      reject
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures       data_only_with_strategy1_and_strategy2_expenditures -1218230.6941    1.0 -20602354.0612   18165892.673  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures -1597059.3608 0.9994   -8083381.301   4889262.5795  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures -1040654.2774    1.0  -6666054.3954   4584745.8405  False
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures -2922960.6941    1.0 -22307084.0612   16461162.673  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures  4309712.9976    0.0   2441140.6121   6178285.3831   True
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures 10160844.8762    0.0   8345341.6676  11976348.0849   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  3425033.6074    0.0   2339831.2418    4510235.973   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  4687442.8773 0.6097   -2660293.147  12035178.9017  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  2578669.1241  0.945  -3294057.7385   8451395.9867  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures -2458073.1518    0.0  -3407786.5355  -1508359.7681   True
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy1_expenditures  -378828.6667    1.0 -20801605.9753   20043948.642  False
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy2_expenditures   177576.4167    1.0 -19988300.3848  20343453.2182  False
      data_only_with_strategy1_and_strategy2_expenditures                        data_with_all_type_of_expenditures    -1704730.0    1.0 -29104761.0109  25695301.0109  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures  5527943.6917  0.998   -13927364.67  24983252.0533  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures 11379075.5703 0.7275  -8071207.5275  30829358.6682  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures  4643264.3015 0.9995 -14752485.8737  24039014.4768  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  5905673.5714 0.9979 -14806802.9915  26618150.1344  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures  3796899.8182    1.0 -16439364.2019  24033163.8382  False
      data_only_with_strategy1_and_strategy2_expenditures                               data_with_zero_expenditures -1239842.4577    1.0 -20628483.8798  18148798.9645  False
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures   556405.0833    1.0  -7987055.6191   9099865.7858  False
                    data_only_with_strategy1_expenditures                        data_with_all_type_of_expenditures -1325901.3333    1.0  -21748678.642  19096875.9753  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures  5906772.3583  0.143   -789282.9765  12602827.6932  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  11757904.237    0.0   5076463.8093  18439344.6646   True
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures  5022092.9682  0.316  -1498893.0356   11543078.972  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  6284502.2381 0.5965  -3479452.8504  16048457.3266  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures  4175728.4848 0.9045  -4532573.0195  12884029.9892  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures   -861013.791    1.0  -7360825.3113   5638797.7292  False
                    data_only_with_strategy2_expenditures                        data_with_all_type_of_expenditures -1882306.4167    1.0 -22048183.2182  18283570.3848  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   5350367.275 0.1117   -515628.9083  11216363.4583  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures 11201499.1536    0.0   5352191.4713   17050806.836   True
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures  4465687.8849  0.282  -1199646.4069  10131022.1766  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  5728097.1548 0.6471  -3486433.3892  14942627.6987  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures  3619323.4015 0.9378  -4468152.6736  11706799.4766  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures -1417418.8743 0.9993  -7058367.7284   4223529.9797  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures  7232673.6917 0.9828   -12222634.67  26687982.0533  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures 13083805.5703 0.5287  -6366477.5275  32534088.6682  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures  6347994.3015 0.9936 -13047755.8737  25743744.4768  False
                       data_with_all_type_of_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  7610403.5714 0.9843 -13102072.9915  28322880.1344  False
                       data_with_all_type_of_expenditures           data_with_strategy3_and_salesVisit_expenditures  5501629.8182 0.9986 -14734634.2019  25737893.8382  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures   464887.5423    1.0 -18923753.8798  19853528.9645  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  5851131.8786    0.0   3389255.2295   8313008.5278   True
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -884679.3901 0.9395  -2870239.4898   1100880.7096  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   377729.8798    1.0  -7155795.8167   7911255.5762  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -1731043.8735  0.998  -7834625.2433   4372537.4964  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures -6767786.1493    0.0  -8682659.5776  -4852912.7211   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures -6735811.2688    0.0  -8671512.1884  -4800110.3492   True
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -5473401.9989 0.4035 -12993940.4477   2047136.4499  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -7582175.7521  0.003 -13669719.9761  -1494631.5281   True
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -12618918.028    0.0 -14482041.5846 -10755794.4714   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  1262409.2699    1.0  -6115944.9391   8640763.4789  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -846364.4833    1.0  -6757354.8363   5064625.8696  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -5883106.7592    0.0  -7046223.9764   -4719989.542   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -2108773.7532 0.9997 -11476343.7403   7258796.2338  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -7145516.0291 0.0661 -14505162.9072     214130.849  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -5036742.2759  0.176 -10924364.7408    850880.1891  False
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_all_type_of_strategies[data_with_all_type_of_strategies['compBrand'] == 5],"Overall strategies with 5 compBrand")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                              
==============================================================================================================================================================================
                          group1                                                    group2                             meandiff    p-adj      lower          upper      reject
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -4269743.0949    1.0 -30984529.4858   22445043.296  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures   4680091.1551 0.9839   -8755952.729  18116135.0393  False
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures  10220464.9051 0.9696 -16494321.4858   36935251.296  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   3697030.1718 0.8181  -3388243.2619  10782303.6055  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  22206850.3426    0.0  15333776.7148  29079923.9705   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   9531106.8467    0.0   6702928.0135  12359285.6799   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   1083110.1051    1.0 -10957810.6576  13124030.8678  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   6811004.2385 0.9276  -8673389.3885  22295397.8655  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures  -3214766.5703 0.1819  -7017840.3794    588307.2388  False
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures     8949834.25 0.9945 -20859351.6008  38759020.1008  False
                    data_only_with_strategy1_expenditures                        data_with_all_type_of_expenditures     14490208.0 0.9687 -23215760.9936  52196176.9936  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures   7966773.2667 0.9958 -19569773.0938  35503319.6272  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  26476593.4375   0.07  -1006117.9813  53959304.8563  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures  13800849.9416 0.8282 -12958426.6059  40560126.4891  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures      5352853.2 0.9999 -23854064.7929  34559771.1929  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures  11080747.3333 0.9798 -19706047.4305  41867542.0971  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures   1054976.5246    1.0 -25824823.6278  27934776.6769  False
                    data_only_with_strategy2_expenditures                        data_with_all_type_of_expenditures     5540373.75 0.9999 -24268812.1008  35349559.6008  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   -983060.9833    1.0 -15986688.8383  14020566.8716  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  17526759.1875 0.0079   2622166.2621  32431352.1129   True
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures   4851015.6916 0.9803   -8673271.524  18375302.9073  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    -3596981.05 0.9998 -21482492.5605  14288530.4605  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures   2130913.0833    1.0 -18232637.5691  22494463.7358  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures  -7894857.7254   0.72 -21656075.6794   5866360.2286  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures  -6523434.7333 0.9991 -34059981.0938  21013111.6272  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  11986385.4375 0.9311 -15496325.9813  39469096.8563  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures   -689358.0584    1.0 -27448634.6059  26069918.4891  False
                       data_with_all_type_of_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures     -9137354.8 0.9925 -38344272.7929  20069563.1929  False
                       data_with_all_type_of_expenditures           data_with_strategy3_and_salesVisit_expenditures  -3409460.6667    1.0 -34196255.4305  27377334.0971  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures -13435231.4754 0.8533 -40315031.6278  13444568.6769  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  18509820.1708    0.0    8927507.577  28092132.7647   True
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   5834076.6749 0.2412  -1417141.7789  13085295.1288  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -2613920.0667 0.9999 -16382193.2469  11154353.1136  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   3113974.0667 0.9999 -13748647.8988  19976596.0321  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures  -6911796.7421 0.1203  -14595867.535    772274.0508  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures -12675743.4959    0.0 -19719762.9825  -5631724.0093   True
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -21123740.2375 0.0001 -34784025.2941  -7463455.1809   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -15395846.1042 0.1036 -32170412.0005   1378719.7922  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -25421616.9129    0.0   -32910474.33 -17932759.4958   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -8447996.7416 0.4499 -20587306.5345   3691313.0513  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -2720102.6083 0.9999 -18281128.1346   12840922.918  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -12745873.417    0.0 -16849827.5873  -8641919.2467   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   5727894.1333 0.9953 -13743384.5286  25199172.7953  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures  -4297876.6754 0.9845  -16700602.873   8104849.5222  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -10025770.8087 0.5845 -25793151.3875     5741609.77  False
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


data_with_strategies_Pharmacy = data_with_all_type_of_strategies[data_with_all_type_of_strategies['accType'] =='Pharmacy']
data_with_strategies_Hospital = data_with_all_type_of_strategies[data_with_all_type_of_strategies['accType'] =='Hospital']
data_with_strategies_Polyclinic = data_with_all_type_of_strategies[data_with_all_type_of_strategies['accType'] =='Polyclinic']
data_with_strategies_Private_Clinic = data_with_all_type_of_strategies[data_with_all_type_of_strategies['accType'] =='Private Clinic']

data_with_strategies_Pharmacy_4 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Pharmacy') & (data_with_all_type_of_strategies['compBrand'] == 4)]
data_with_strategies_Hospital_4 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Hospital') & (data_with_all_type_of_strategies['compBrand'] == 4)]
data_with_strategies_Polyclinic_4 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Polyclinic') & (data_with_all_type_of_strategies['compBrand'] == 4)]
data_with_strategies_Private_Clinic_4 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Private Clinic') & (data_with_all_type_of_strategies['compBrand'] == 4)]


data_with_strategies_Pharmacy_5 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Pharmacy') & (data_with_all_type_of_strategies['compBrand'] == 5)]
data_with_strategies_Hospital_5 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Hospital') & (data_with_all_type_of_strategies['compBrand'] == 5)]
data_with_strategies_Polyclinic_5 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Polyclinic') & (data_with_all_type_of_strategies['compBrand'] == 5)]
data_with_strategies_Private_Clinic_5 = data_with_all_type_of_strategies[(data_with_all_type_of_strategies['accType'] =='Private Clinic') & (data_with_all_type_of_strategies['compBrand'] == 5)]


tukey_test(data_with_strategies_Pharmacy,"Pharmacy")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
=============================================================================================================================================================================
                          group1                                                    group2                             meandiff    p-adj      lower          upper     reject
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -7009519.9514 0.9996 -40014927.7147 25995887.8119  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures    235584.0486    1.0 -32769823.7147 33240991.8119  False
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures   6628323.0486 0.9998 -26377084.7147 39633730.8119  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   1460554.7343 0.9992   -4738045.021  7659154.4896  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  11515585.8668    0.0   6872017.1609 16159154.5727   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   3807925.1233 0.0383    102482.9738  7513367.2728   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   5207714.7153 0.9974  -13978934.763 24394364.1936  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   5775958.0486 0.9988 -17642728.3804 29194644.4776  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures   -6879727.815 0.0004 -11768938.8249 -1990516.8052   True
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures      7245104.0    1.0 -39270358.7007 53760566.7007  False
                    data_only_with_strategy1_expenditures                        data_with_all_type_of_expenditures     13637843.0 0.9954 -32877619.7007 60153305.7007  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures   8470074.6857 0.9985 -24887892.3921 41828041.7635  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  18525105.8182 0.7486 -14579184.8246  51629396.461  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures  10817445.0747 0.9895 -22168334.1363 43803224.2857  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  12217234.6667 0.9908 -25762481.5887 50196950.9221  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures     12785478.0 0.9917 -27498094.3676 53069050.3676  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures    129792.1364    1.0 -33009847.4654 33269431.7381  False
                    data_only_with_strategy2_expenditures                        data_with_all_type_of_expenditures      6392739.0    1.0 -40122723.7007 52908201.7007  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   1224970.6857    1.0 -32132996.3921 34582937.7635  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  11280001.8182 0.9862 -21824288.8246  44384292.461  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures   3572341.0747    1.0 -29413438.1363 36558120.2857  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   4972130.6667    1.0 -33007585.5887 42951846.9221  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures      5540374.0    1.0 -34743198.3676 45823946.3676  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures  -7115311.8636 0.9996 -40254951.4654 26024327.7381  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures  -5167768.3143    1.0 -38525735.3921 28190198.7635  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   4887262.8182    1.0 -28217027.8246  37991553.461  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures  -2820397.9253    1.0 -35806177.1363 30165381.2857  False
                       data_with_all_type_of_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -1420608.3333    1.0 -39400324.5887 36559107.9221  False
                       data_with_all_type_of_expenditures           data_with_strategy3_and_salesVisit_expenditures      -852365.0    1.0 -41135937.3676 39431207.3676  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures -13508050.8636 0.9544 -46647690.4654 19631588.7381  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  10055031.1325 0.0001   3349827.4618 16760234.8031   True
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures    2347370.389 0.9683  -3745849.4053  8440590.1833  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    3747159.981 0.9999 -16039819.1756 23534139.1375  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   4315403.3143 0.9999 -19597603.6066 28228410.2351  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures  -8340282.5494 0.0051 -15217884.6196 -1462680.4791   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -7707660.7435    0.0 -12209595.7566 -3205725.7304   True
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -6307871.1515 0.9899 -25664126.8727 13048384.5696  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -5739627.8182 0.9989   -29297471.34 17818215.7036  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -18395313.6818    0.0 -23912695.4556 -12877931.908   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    1399789.592    1.0 -17753074.5996 20552653.7835  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   1968032.9253    1.0 -21422981.6443 25359047.4949  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -10687652.9383    0.0 -15442552.8147  -5932753.062   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures    568243.3333    1.0 -29457358.7302 30593845.3968  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -12087442.5303 0.6146 -31504092.3318  7329207.2712  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -12655685.8636 0.7931 -36263177.1613  10951805.434  False
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Pharmacy_4,"Pharmacy with 4 compBrand")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                              
==============================================================================================================================================================================
                          group1                                                    group2                             meandiff    p-adj      lower          upper      reject
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -6175773.3471 0.9991 -34875561.0489  22524014.3546  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures   1069330.6529    1.0 -27630457.0489  29769118.3546  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   2270632.9654 0.9456  -3410951.8608   7952217.7916  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  10322658.9711    0.0   5948972.9326  14696345.0095   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   1682705.9606 0.8761  -1927765.8078   5293177.7289  False
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   6041461.3196 0.9698 -10663634.9997  22746557.6389  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   6609704.6529 0.9847 -13767111.4823  26986520.7881  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures  -6201743.6752 0.0005 -10619427.6629  -1784059.6875   True
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures      7245104.0 0.9998 -33175839.9927  47666047.9927  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures   8446406.3125 0.9925 -20578674.3123  37471486.9373  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  16498432.3182 0.6911 -12299206.9723  45296071.6086  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures   7858479.3077  0.995 -20833164.1714  36550122.7868  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  12217234.6667 0.9652 -20786327.9013  45220797.2346  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures     12785478.0 0.9679 -22220086.3427  47791042.3427  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures    -25970.3281    1.0  -28830324.703  28778384.0468  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   1201302.3125    1.0 -27823778.3123  30226382.9373  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   9253328.3182 0.9856 -19544310.9723  38050967.6086  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures    613375.3077    1.0 -28078268.1714  29305018.7868  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   4972130.6667 0.9999 -28031431.9013  37975693.2346  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures      5540374.0 0.9999 -29465190.3427  40545938.3427  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures  -7271074.3281 0.9972  -36075428.703  21533280.0468  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   8052026.0057 0.0018   1895190.7216  14208861.2897   True
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   -587927.0048    1.0  -6228228.1973   5052374.1877  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   3770828.3542  0.999 -13487147.6045  21028804.3128  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   4339071.6875 0.9993 -16493406.0087  25171549.3837  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures  -8472376.6406 0.0008 -14660544.6221  -2284208.6591   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -8639953.0105    0.0  -12959874.418  -4320031.6029   True
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -4281197.6515 0.9971 -21153851.7881  12591456.4851  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -3712954.3182 0.9997 -24227360.2415  16801451.6051  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -16524402.6463    0.0 -21538597.1372 -11510208.1554   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    4358755.359 0.9964 -12332345.0973  21049855.8152  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   4926998.6923 0.9979 -15438345.0861  25292342.4707  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -7884449.6358    0.0 -12248911.0252  -3519988.2464   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures    568243.3333    1.0 -25523363.8203   26659850.487  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -12243204.9948 0.3684 -29127317.6415   4640907.6519  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -12811448.3281 0.5817 -33335279.6624   7712383.0062  False
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Pharmacy_5,"Pharmacy with 5 compBrand")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
=============================================================================================================================================================================
                          group1                                                    group2                             meandiff    p-adj      lower          upper     reject
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures    2242090.913    1.0 -33844691.9504 38328873.7765  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures  -2673214.7536 0.9992 -24358672.4171 19012242.9098  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  19289394.7312 0.0006   6338920.8144  32239868.648   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   8164091.8449 0.1036   -925693.1065 17253876.7962  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures   -6281561.087 0.9809 -32324953.3375 19761831.1636  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures  -4915305.6667 0.9993 -45707383.9016 35876772.5682  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  17047303.8182 0.7562  -19850519.252 53945126.8884  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures   5922000.9318 0.9966 -29804162.6337 41648164.4974  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures     -8523652.0 0.9924 -51790184.7079 34742880.7079  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  21962609.4848   0.07  -1047225.8896 44972444.8593  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  10837306.5985  0.664 -10242587.6078 31917200.8048  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures  -3608346.3333 0.9995 -35857315.7619 28640623.0952  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures -11125302.8864 0.0808 -23034024.0749   783418.3021  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -25570955.8182 0.0767 -52727018.3052  1585106.6688  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -14445652.9318 0.5669 -39987014.3989 11095708.5352  False
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Hospital,"Hospital")

                                                            Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
============================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower          upper     reject
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures       data_only_with_strategy1_and_strategy2_expenditures -2448045.3011    1.0 -23475057.2897 18578966.6874  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures -1382588.5511    1.0 -11921418.1614  9156241.0591  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures  3802633.0322 0.9428  -4816042.1842 12421308.2486  False
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures -4152775.3011 0.9999 -25179787.2897 16874236.6874  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   2990700.578 0.0023    632386.3529  5349014.8031   True
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  5280509.9957    0.0   2522234.3253  8038785.6662   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  3775130.4062    0.0   2341033.3169  5209227.4954   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   950761.1989    1.0  -6525125.3005  8426647.6982  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  1426342.2443 0.9998   -4964300.892  7816985.3807  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures -3106158.6702    0.0  -4720269.4755 -1492047.8649   True
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy1_expenditures    1065456.75    1.0 -22424551.8925 24555465.3925  False
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy2_expenditures  6250678.3333 0.9984 -16442826.0006 28944182.6673  False
      data_only_with_strategy1_and_strategy2_expenditures                        data_with_all_type_of_expenditures    -1704730.0    1.0 -31417501.8269 28008041.8269  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures  5438745.8791 0.9991 -15686481.2783 26563973.0366  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  7728555.2969 0.9849 -13445052.3585 28902162.9522  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures  6223175.7073 0.9972 -14818929.9604  27265281.375  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures     3398806.5    1.0 -18885772.3702 25683385.3702  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures  3874387.5455    1.0 -18069948.7663 25818723.8573  False
      data_only_with_strategy1_and_strategy2_expenditures                               data_with_zero_expenditures  -658113.3691    1.0 -21713253.6004 20397026.8622  False
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures  5185221.5833 0.9787  -8376741.2297 18747184.3963  False
                    data_only_with_strategy1_expenditures                        data_with_all_type_of_expenditures   -2770186.75    1.0 -26260195.3925 20719821.8925  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures  4373289.1291 0.9666  -6360159.4705 15106737.7287  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  6663098.5469 0.6603  -4165260.4022 17491457.4959  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures  5157718.9573 0.8934   -5411193.348 15726631.2626  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    2333349.75    1.0 -10532657.8595 15199357.3595  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures  2808930.7955 0.9997  -9458326.1331  15076187.724  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures -1723570.1191    1.0 -12318409.7379  8871269.4997  False
                    data_only_with_strategy2_expenditures                        data_with_all_type_of_expenditures -7955408.3333 0.9889 -30648912.6673 14738096.0006  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures  -811932.4542    1.0  -9667526.8313  8043661.9229  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  1477876.9635    1.0  -7492518.2597 10448272.1868  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures    -27502.626    1.0  -8682936.7716  8627931.5196  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -2851871.8333 0.9993 -14198624.0003  8494880.3336  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures -2376290.7879 0.9998 -13039321.5711  8286739.9953  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures -6908791.7024 0.2688 -15595865.9786  1778282.5737  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures  7143475.8791 0.9916 -13981751.2783 28268703.0366  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  9433285.2969 0.9391 -11740322.3585 30606892.9522  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures  7927905.7073 0.9809 -13114199.9604  28970011.375  False
                       data_with_all_type_of_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures     5103536.5 0.9997 -17181042.3702 27388115.3702  False
                       data_with_all_type_of_expenditures           data_with_strategy3_and_salesVisit_expenditures  5579117.5455 0.9992 -16365218.7663 27523453.8573  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures  1046616.6309    1.0 -20008523.6004 22101756.8622  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  2289809.4178 0.5384  -1137736.9292  5717355.7647  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   784429.8282 0.9952  -1704872.0313  3273731.6877  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -2039939.3791 0.9989  -9787769.2842  5707890.5259  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -1564358.3337 0.9996  -8271094.4238  5142377.7565  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures -6096859.2482    0.0  -8694039.0606 -3499679.4358   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures -1505379.5896 0.8406  -4376452.9842  1365693.8051  False
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -4329748.7969 0.7967 -12208537.2144  3549039.6206  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -3854167.7514 0.7731 -10711772.8489   3003437.346  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -8386668.666    0.0 -11351762.5377 -5421574.7943   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -2824369.2073 0.9813 -10342604.1541  4693865.7394  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -2348788.1619  0.985  -8788919.9508  4091343.6271  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -6881289.0764    0.0  -8681381.1448  -5081197.008   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   475581.0455    1.0  -9286977.1208 10238139.2117  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -4056919.8691 0.8192 -11611559.2347  3497719.4965  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -4532500.9146 0.4664  -11015094.276  1950092.4469  False
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Hospital_4,"Hospital with 4 compBrand")

                                                            Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
============================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower          upper     reject
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures       data_only_with_strategy1_and_strategy2_expenditures -2049228.2996    1.0 -21473401.2963 17374944.6971  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -983771.5496    1.0 -10724663.1132   8757120.014  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures  -855916.6996    1.0  -9577006.0759  7865172.6767  False
                   data_only_with_salesVisit_expenditures                        data_with_all_type_of_expenditures -3753958.2996 0.9999 -23178131.2963 15670214.6971  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures  3150725.8115 0.0007     827816.409   5473635.214   True
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  5178539.9038    0.0   2508455.1934  7848624.6142   True
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  2810763.6846    0.0   1317575.3349  4303952.0344   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   934050.3671    1.0 -10302686.2304 12170786.9645  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   -60375.8552    1.0  -6586183.6169  6465431.9066  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures -2706599.9982    0.0  -4277121.6913 -1136078.3052   True
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy1_expenditures    1065456.75    1.0 -20629916.3417 22760829.8417  False
      data_only_with_strategy1_and_strategy2_expenditures                     data_only_with_strategy2_expenditures     1193311.6    1.0 -20063725.9416 22450349.1416  False
      data_only_with_strategy1_and_strategy2_expenditures                        data_with_all_type_of_expenditures    -1704730.0    1.0 -29147447.4628 25737987.4628  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures  5199954.1111 0.9988 -14324393.5919 24724301.8141  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  7227768.2034 0.9834 -12340921.0091 26796457.4159  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures  4859991.9843 0.9993 -14583100.7889 24303084.7574  False
      data_only_with_strategy1_and_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  2983278.6667    1.0 -19423606.3131 25390163.6464  False
      data_only_with_strategy1_and_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures  1988852.4444    1.0 -18465741.4669 22443446.3558  False
      data_only_with_strategy1_and_strategy2_expenditures                               data_with_zero_expenditures  -657371.6986    1.0  -20106556.348 18791812.9507  False
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures     127854.85    1.0  -12889369.005  13145078.705  False
                    data_only_with_strategy1_expenditures                        data_with_all_type_of_expenditures   -2770186.75    1.0 -24465559.8417 18925186.3417  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures  4134497.3611 0.9613  -5804648.7327 14073643.4549  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  6162311.4534 0.6614  -3863658.2924 16188281.1991  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures  3794535.2343 0.9762  -5984029.5086 13573099.9771  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  1917821.9167    1.0 -12902939.4113 16738583.2447  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures   923395.6944    1.0 -10737516.9596 12584308.3485  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures -1722828.4486    1.0 -11513500.3035  8067843.4063  False
                    data_only_with_strategy2_expenditures                        data_with_all_type_of_expenditures    -2898041.6    1.0 -24155079.1416 18358995.9416  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures  4006642.5111 0.9367  -4935340.2965 12948625.3187  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  6034456.6034 0.5389  -3003933.7842  15072846.991  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures  3666680.3843 0.9597  -5096467.4455  12429828.214  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  1789967.0667    1.0 -12381391.2944 15961325.4277  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures   795540.8444    1.0 -10028012.8872 11619094.5761  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures -1850683.2986 0.9999  -10627339.087  6925972.4897  False
                       data_with_all_type_of_expenditures           data_with_strategy1_and_salesVisit_expenditures  6904684.1111 0.9881 -12619663.5919 26429031.8141  False
                       data_with_all_type_of_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  8932498.2034 0.9287 -10636191.0091 28501187.4159  False
                       data_with_all_type_of_expenditures           data_with_strategy2_and_salesVisit_expenditures  6564721.9843 0.9917 -12878370.7889 26007814.7574  False
                       data_with_all_type_of_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  4688008.6667 0.9999 -17718876.3131 27094893.6464  False
                       data_with_all_type_of_expenditures           data_with_strategy3_and_salesVisit_expenditures  3693582.4444    1.0 -16761011.4669 24148176.3558  False
                       data_with_all_type_of_expenditures                               data_with_zero_expenditures  1047358.3014    1.0  -18401826.348 20496542.9507  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  2027814.0923 0.6706   -1293482.188  5349110.3726  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -339962.1269    1.0  -2816101.8783  2136177.6246  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -2216675.4444 0.9999 -13625702.8342  9192351.9453  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -3211101.6667 0.9135 -10029299.6371  3607096.3038  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures -5857325.8097    0.0  -8380854.0955  -3333797.524   True
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures -2367776.2191 0.1907  -5172185.6645   436633.2262  False
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -4244489.5367 0.9834 -15729233.7763  7240254.7029  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -5238915.7589 0.3461  -12183069.259  1705237.7412  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -7885139.902    0.0 -10731477.7733 -5038802.0308   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -1876713.3176    1.0 -13146123.6542  9392697.0191  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures -2871139.5398 0.9468  -9453048.6033  3710769.5237  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -5517363.6829    0.0  -7306748.5952 -3727978.7706   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  -994426.2222    1.0  -13931047.297 11942194.8525  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -3640650.3653 0.9942 -14920567.7531  7639267.0225  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -2646224.1431   0.97  -9246107.0102  3953658.7241  False
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Hospital_5,"Hospital with 5 compBrand")

                                                             Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
=============================================================================================================================================================================
                          group1                                                    group2                             meandiff    p-adj      lower          upper     reject
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures  27371485.7692 0.0114   3689431.9122 51053539.6262   True
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   3206932.9692 0.9113  -4562337.1528 10976203.0913  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   9471816.7692 0.1306  -1297131.0836 20240764.6221  False
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures    6736185.499    0.0   3233677.1068 10238693.8911   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures   -517903.0308    1.0 -11286850.8836 10251044.8221  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   8193269.2692 0.8116  -8623278.4366 25009816.9751  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures  -4835741.4451 0.3444 -11504576.7294  1833093.8393  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures    -24164552.8 0.0608 -48897030.9596   567925.3596  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures    -17899669.0  0.405 -43731899.9439  7932561.9439  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures -20635300.2703 0.1407 -44375592.6594  3104992.1188  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    -27889388.8 0.0242 -53721619.7439 -2057157.8561   True
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures    -19178216.5 0.4627 -48059528.7006  9703095.7006  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures -32207227.2143 0.0019 -56616391.1027 -7798063.3258   True
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures      6264883.8 0.8151   -6651231.672  19180999.272  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   3529252.5297 0.8744  -4415768.9344 11474273.9938  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures     -3724836.0 0.9874  -16640951.472   9191279.472  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures      4986336.3 0.9909 -13279809.3736 23252481.9736  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures  -8042674.4143 0.1922 -17806339.9697  1720991.1411  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -2735631.2703 0.9945 -13632054.7979  8160792.2573  False
data_with_strategy1_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures     -9989719.8 0.4509 -24903965.2893  4924525.6893  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures     -1278547.5    1.0 -21008239.7784 18451144.7784  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -14307558.2143 0.0104 -26593234.6184 -2021881.8102   True
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures  -7254088.5297 0.4592 -18150512.0573  3642334.9979  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   1457083.7703    1.0 -15441380.2007 18355547.7413  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -11571926.944    0.0 -18444711.8152 -4699142.0729   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures      8711172.3 0.8779 -11018519.9784 28440864.5784  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures  -4317838.4143 0.9613 -16603514.8184  7967837.9898  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures -13029010.7143  0.334 -30854943.6097  4796922.1811  False
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Polyclinic,"Polyclinic")

                                                           Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                            
==========================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower        upper     reject
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -498572.4258 0.9976 -3089957.4671 2092812.6155  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures  -133273.1401    1.0 -1526647.2771 1260100.9968  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures   779975.4075 0.7245  -723269.0222 2283219.8372  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   353792.9075 0.9989 -1764581.4984 2472167.3134  False
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   347236.0819 0.3503   -140224.146  834696.3097  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures  -298681.4907 0.0172  -565902.3713  -31460.6102   True
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures   365299.2857 0.9998 -2566050.7282 3296649.2996  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures  1278547.8333 0.8673  -1706592.906 4263688.5727  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   852365.3333 0.9889 -2485123.4745 4189854.1412  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures   845808.5077 0.9637 -1778870.3093 3470487.3247  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures   199890.9351    1.0 -2392931.5458  2792713.416  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures   913248.5476 0.8392 -1120782.0046 2947279.0998  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   487066.0476 0.9976 -2035838.3492 3009970.4445  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures    480509.222  0.959  -973847.6619 1934866.1058  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures  -165408.3506 0.9999 -1561454.0056 1230637.3044  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures     -426182.5  0.999 -3011390.2142 2159025.2142  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -432739.3256 0.9829 -1992677.2332 1127198.5819  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures -1078656.8982 0.3433 -2584377.9217  427064.1253  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures    -6556.8256    1.0 -2165531.7175 2152418.0663  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -652474.3982 0.9711  -2772606.967 1467658.1705  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -645917.5726 0.0024 -1140962.4788 -150872.6664   True
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Polyclinic_4,"Polyclinic with 4 compBrand")

                                                           Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                            
==========================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower        upper     reject
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures  -343736.5327 0.9999 -3514222.8978 2826749.8323  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures  -343736.5327 0.9955 -1936020.0601 1248546.9946  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures  1190520.8673 0.1726  -235753.6625  2616795.397  False
                   data_only_with_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   508628.8006  0.983 -1327276.0155 2344533.6167  False
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   188991.7173 0.8999  -282772.6447  660756.0793  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures  -221155.5996  0.128  -472843.4247   30532.2254  False
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures           0.0    1.0 -3539448.3938 3539448.3938  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures     1534257.4 0.8481 -1933679.6143 5002194.4143  False
                    data_only_with_strategy1_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   852365.3333 0.9932  -2803161.249 4507891.9157  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures     532728.25 0.9989 -2663345.8596 3728802.3596  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures   122580.9331    1.0 -3048487.4771 3293649.3433  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures     1534257.4 0.3324  -589411.6363 3657926.4363  False
                    data_only_with_strategy2_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures   852365.3333 0.9439 -1565538.2286 3270268.8953  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures     532728.25 0.9624 -1109913.7544 2175370.2544  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures   122580.9331    1.0 -1470861.2223 1716023.0885  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy1_strategy2_and_salesVisit_expenditures  -681892.0667 0.9766 -2993850.0762 1630065.9428  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures   -1001529.15 0.4168 -2483812.8466  480754.5466  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures -1411676.4669 0.0549 -2839244.3656   15891.4318  False
data_with_strategy1_strategy2_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  -319637.0833 0.9988 -2199385.0496  1560110.883  False
data_with_strategy1_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -729784.4002 0.9035  -2566694.187 1107125.3866  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures  -410147.3169 0.1435  -885807.5784   65512.9446  False
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Polyclinic_5,"Polyclinic with 5 compBrand")

                                                 Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                  
======================================================================================================================================================
                     group1                                          group2                        meandiff   p-adj      lower        upper     reject
------------------------------------------------------------------------------------------------------------------------------------------------------
         data_only_with_salesVisit_expenditures           data_only_with_strategy1_expenditures  -1133021.939 0.9863 -6130200.8892 3864157.0112  False
         data_only_with_salesVisit_expenditures           data_only_with_strategy2_expenditures   -280656.939 0.9998 -3200333.1494 2639019.2713  False
         data_only_with_salesVisit_expenditures data_with_strategy1_and_salesVisit_expenditures  -1133021.939 0.9863 -6130200.8892 3864157.0112  False
         data_only_with_salesVisit_expenditures data_with_strategy2_and_salesVisit_expenditures   965107.5994 0.4178  -517669.8831  2447885.082  False
         data_only_with_salesVisit_expenditures                     data_with_zero_expenditures   -355238.739 0.8916 -1313173.1524  602695.6744  False
          data_only_with_strategy1_expenditures           data_only_with_strategy2_expenditures      852365.0 0.9981 -4883014.3749 6587744.3749  False
          data_only_with_strategy1_expenditures data_with_strategy1_and_salesVisit_expenditures           0.0    1.0 -7024376.4749 7024376.4749  False
          data_only_with_strategy1_expenditures data_with_strategy2_and_salesVisit_expenditures  2098129.5385 0.8471 -3056353.6038 7252612.6807  False
          data_only_with_strategy1_expenditures                     data_with_zero_expenditures      777783.2 0.9977 -4250905.0725 5806471.4725  False
          data_only_with_strategy2_expenditures data_with_strategy1_and_salesVisit_expenditures     -852365.0 0.9981 -6587744.3749 4883014.3749  False
          data_only_with_strategy2_expenditures data_with_strategy2_and_salesVisit_expenditures  1245764.5385 0.8671 -1935651.5262 4427180.6032  False
          data_only_with_strategy2_expenditures                     data_with_zero_expenditures      -74581.8    1.0 -3047865.7512 2898702.1512  False
data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_and_salesVisit_expenditures  2098129.5385 0.8471 -3056353.6038 7252612.6807  False
data_with_strategy1_and_salesVisit_expenditures                     data_with_zero_expenditures      777783.2 0.9977 -4250905.0725 5806471.4725  False
data_with_strategy2_and_salesVisit_expenditures                     data_with_zero_expenditures -1320346.3385 0.1611 -2906075.6167  265382.9398  False
------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Private_Clinic,"Private_Clinic")

                                                            Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
============================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower          upper     reject
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures -1750244.9612 0.9793  -7315824.0338  3815334.1115  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures -1750244.9612 0.9936  -8534430.2964   5033940.374  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures  -613757.9612    1.0  -6179337.0338  4951821.1115  False
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  2484085.2646 0.0031    537427.5091  4430743.0201   True
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -1750244.9612 0.9993 -11298735.4915  7798245.5691  False
                   data_only_with_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures  3363946.0388 0.9611  -6184544.4915 12912436.5691  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures -1650802.3612 0.0025  -2927181.9794   -374422.743   True
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures           0.0    1.0  -8674531.7592  8674531.7592  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures     1136487.0 0.9998  -6622250.0746  8895224.0746  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures  4234330.2258 0.3244  -1511259.9959  9979920.4476  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures           0.0    1.0 -10972511.1978 10972511.1978  False
                    data_only_with_strategy1_expenditures           data_with_strategy3_and_salesVisit_expenditures     5114191.0 0.8453  -5858320.1978 16086702.1978  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures       99442.6    1.0  -5454967.8556  5653853.0556  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures     1136487.0 0.9999  -7538044.7592  9811018.7592  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures  4234330.2258 0.5751  -2698296.0619 11166956.5135  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures           0.0    1.0 -11638105.6119 11638105.6119  False
                    data_only_with_strategy2_expenditures           data_with_strategy3_and_salesVisit_expenditures     5114191.0 0.8814  -6523914.6119 16752296.6119  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures       99442.6    1.0  -6675583.2861  6874468.4861  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  3097843.2258 0.7207  -2647746.9959  8843433.4476  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    -1136487.0    1.0 -12108998.1978  9836024.1978  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures     3977704.0 0.9547  -6994807.1978 14950215.1978  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures    -1037044.4 0.9992  -6591454.8556  4517366.0556  False
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -4234330.2258 0.8825  -13888852.896  5420192.4444  False
          data_with_strategy2_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures   879860.7742    1.0   -8774661.896 10534383.4444  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -4134887.6258    0.0  -6049380.1205 -2220395.1312   True
data_with_strategy2_strategy3_and_salesVisit_expenditures           data_with_strategy3_and_salesVisit_expenditures     5114191.0 0.9415  -8324335.8158 18552717.8158  False
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures       99442.6    1.0  -9442542.3351  9641427.5351  False
          data_with_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures    -5014748.4 0.7463 -14556733.3351  4527236.5351  False
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Private_Clinic_4,"Private_Clinic with 4 compBrand")

                                                            Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                             
============================================================================================================================================================================
                          group1                                                    group2                             meandiff   p-adj      lower          upper     reject
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy1_expenditures -2243377.7639 0.9014  -7907853.6352  3421098.1074  False
                   data_only_with_salesVisit_expenditures                     data_only_with_strategy2_expenditures -2243377.7639 0.9601  -9134510.0806  4647754.5528  False
                   data_only_with_salesVisit_expenditures           data_with_strategy1_and_salesVisit_expenditures  -538647.2639    1.0  -7429779.5806  6352485.0528  False
                   data_only_with_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures  1711596.6361  0.257    -519948.059  3943141.3312  False
                   data_only_with_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures -2243377.7639 0.9931 -11922838.3758   7436082.848  False
                   data_only_with_salesVisit_expenditures                               data_with_zero_expenditures -2198906.5378 0.0002   -3643553.105  -754259.9706   True
                    data_only_with_strategy1_expenditures                     data_only_with_strategy2_expenditures           0.0    1.0  -8775368.2858  8775368.2858  False
                    data_only_with_strategy1_expenditures           data_with_strategy1_and_salesVisit_expenditures     1704730.5 0.9974  -7070637.7858 10480098.7858  False
                    data_only_with_strategy1_expenditures           data_with_strategy2_and_salesVisit_expenditures     3954974.4  0.415  -1918625.4903  9828574.2903  False
                    data_only_with_strategy1_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures           0.0    1.0 -11100060.4359 11100060.4359  False
                    data_only_with_strategy1_expenditures                               data_with_zero_expenditures    44471.2261    1.0  -5577484.6301  5666427.0823  False
                    data_only_with_strategy2_expenditures           data_with_strategy1_and_salesVisit_expenditures     1704730.5 0.9984  -7908203.8211 11317664.8211  False
                    data_only_with_strategy2_expenditures           data_with_strategy2_and_salesVisit_expenditures     3954974.4  0.639  -3109060.8052 11019009.6052  False
                    data_only_with_strategy2_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures           0.0    1.0 -11773392.0087 11773392.0087  False
                    data_only_with_strategy2_expenditures                               data_with_zero_expenditures    44471.2261    1.0   -6811752.615  6900695.0672  False
          data_with_strategy1_and_salesVisit_expenditures           data_with_strategy2_and_salesVisit_expenditures     2250243.9 0.9641  -4813791.3052  9314279.1052  False
          data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    -1704730.5 0.9995 -13478122.5087 10068661.5087  False
          data_with_strategy1_and_salesVisit_expenditures                               data_with_zero_expenditures -1660259.2739 0.9912   -8516483.115  5195964.5672  False
          data_with_strategy2_and_salesVisit_expenditures data_with_strategy2_strategy3_and_salesVisit_expenditures    -3954974.4 0.8932 -13758282.3372  5848333.5372  False
          data_with_strategy2_and_salesVisit_expenditures                               data_with_zero_expenditures -3910503.1739    0.0  -6031798.1743 -1789208.1735   True
data_with_strategy2_strategy3_and_salesVisit_expenditures                               data_with_zero_expenditures    44471.2261    1.0   -9610167.995  9699110.4472  False
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------


tukey_test(data_with_strategies_Private_Clinic_5,"Private_Clinic with 5 compBrand")

                                                  Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                   
========================================================================================================================================================
                     group1                                          group2                        meandiff   p-adj      lower          upper     reject
--------------------------------------------------------------------------------------------------------------------------------------------------------
         data_only_with_salesVisit_expenditures data_with_strategy1_and_salesVisit_expenditures  -604904.2581 0.9986  -6759677.0707  5549868.5546  False
         data_only_with_salesVisit_expenditures data_with_strategy2_and_salesVisit_expenditures  4793408.5753 0.0001   2091552.1052  7495265.0453   True
         data_only_with_salesVisit_expenditures data_with_strategy3_and_salesVisit_expenditures  4509286.7419 0.2427  -1645486.0707 10664059.5546  False
         data_only_with_salesVisit_expenditures                     data_with_zero_expenditures   758879.9419 0.9448  -2160585.1434  3678345.0273  False
data_with_strategy1_and_salesVisit_expenditures data_with_strategy2_and_salesVisit_expenditures  5398312.8333 0.1485  -1144903.5002 11941529.1668  False
data_with_strategy1_and_salesVisit_expenditures data_with_strategy3_and_salesVisit_expenditures     5114191.0 0.4416  -3452890.1819 13681272.1819  False
data_with_strategy1_and_salesVisit_expenditures                     data_with_zero_expenditures     1363784.2 0.9761  -5272248.3486  7999816.7486  False
data_with_strategy2_and_salesVisit_expenditures data_with_strategy3_and_salesVisit_expenditures  -284121.8333 0.9999  -6827338.1668  6259094.5002  False
data_with_strategy2_and_salesVisit_expenditures                     data_with_zero_expenditures -4034528.6333 0.0249  -7702733.6434  -366323.6233   True
data_with_strategy3_and_salesVisit_expenditures                     data_with_zero_expenditures    -3750406.8 0.4965 -10386439.3486  2885625.7486  False
--------------------------------------------------------------------------------------------------------------------------------------------------------

	count	mean	std	min	25%	50%	75%	max
accSize	2976.00	364.23	417.46	0.00	60.00	240.00	522.50	2460.00
accTargets	2976.00	15.02	17.41	1.00	2.00	9.50	20.25	102.00
district	2976.00	3.00	0.00	3.00	3.00	3.00	3.00	3.00
sales	2976.00	4251099.72	7564507.53	-5540374.00	0.00	852365.00	5114191.00	51994276.00
qty	2976.00	40.49	72.14	-21.00	0.00	8.00	49.00	500.00
strategy1	2976.00	33322.08	168150.04	0.00	0.00	0.00	0.00	2662031.00
strategy2	2976.00	159399.68	401764.51	0.00	0.00	0.00	18949.50	3161384.00
strategy3	2976.00	6924.98	89119.39	0.00	0.00	0.00	0.00	2236600.00
salesVisit1	2976.00	30544.35	240670.30	0.00	0.00	0.00	0.00	6750000.00
salesVisit2	2976.00	470539.31	789997.91	0.00	0.00	150000.00	600000.00	7125000.00
salesVisit3	2976.00	434299.40	760778.74	0.00	0.00	75000.00	600000.00	6075000.00
salesVisit4	2976.00	16456.65	89997.03	0.00	0.00	0.00	0.00	1125000.00
salesVisit5	2976.00	3402.22	20711.52	0.00	0.00	0.00	0.00	375000.00
compBrand	2976.00	4.17	0.37	4.00	4.00	4.00	4.00	5.00
total_expenditures	2976.00	1154888.68	1519360.65	0.00	0.00	600000.00	1705311.50	11634764.00
strategy_expenditures	2976.00	199646.74	474005.40	0.00	0.00	0.00	101940.00	4103439.00
salesVisit_expenditures	2976.00	955241.94	1224176.72	0.00	0.00	525000.00	1425000.00	9150000.00
net_income	2976.00	3096211.05	6638601.49	-8674780.00	-225000.00	0.00	3693003.50	46915728.00
net_income_per_unit	2976.00	25124.78	106143.25	-3075479.00	0.00	0.00	80943.65	266364.12
unit_price	2976.00	54280.91	57480.42	-497213.00	0.00	102283.84	104135.11	273974.57

	accType	compBrand	accSize	sales	qty	strategy1	strategy2	strategy3	salesVisit1	salesVisit2	...	salesVisit5	net_income	total_expenditures	strategy_expenditures	salesVisit_expenditures	percentage_profit	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	Hospital	4	283510.00	4957739446.00	47329.00	33774878.00	172244171.00	9664049.00	29775000.00	406425000.00	...	3225000.00	3858706348.00	1099033098.00	215683098.00	883350000.00	351.10	3.07	15.67	0.88	80.38
1	Hospital	5	73658.00	1900987438.00	18007.00	4130388.00	58747188.00	4046342.00	7650000.00	126975000.00	...	375000.00	1592188520.00	308798918.00	66923918.00	241875000.00	515.61	1.34	19.02	1.31	78.33
2	Pharmacy	4	356931.00	3553851383.00	33618.00	40644245.00	127955747.00	4853400.00	37425000.00	401700000.00	...	3525000.00	2507172991.00	1046678392.00	173453392.00	873225000.00	239.54	3.88	12.22	0.46	83.43
3	Pharmacy	5	80976.00	1581904543.00	15200.00	7417608.00	47095630.00	910584.00	9075000.00	136875000.00	...	225000.00	1297055721.00	284848822.00	55423822.00	229425000.00	455.35	2.60	16.53	0.32	80.54
4	Polyclinic	4	18760.00	190290517.00	1805.00	430905.00	2585051.00	0.00	150000.00	34950000.00	...	150000.00	135824561.00	54465956.00	3015956.00	51450000.00	249.38	0.79	4.75	0.00	94.46
5	Polyclinic	5	10368.00	153851906.00	1469.00	0.00	3796453.00	0.00	75000.00	16725000.00	...	0.00	126055453.00	27796453.00	3796453.00	24000000.00	453.49	0.00	13.66	0.00	86.34
6	Private Clinic	4	15336.00	268921211.00	2546.00	77631.00	7439758.00	0.00	0.00	21525000.00	...	375000.00	226378822.00	42542389.00	7517389.00	35025000.00	532.13	0.18	17.49	0.00	82.33
7	Private Clinic	5	3290.00	63075021.00	603.00	0.00	3642558.00	256615.00	75000.00	4275000.00	...	300000.00	50400848.00	12674173.00	3899173.00	8775000.00	397.67	0.00	28.74	2.02	69.24

	accType	compBrand	accSize	strategy1	strategy2	strategy3	salesVisit1	salesVisit2	...	salesVisit5	net_income	total_expenditures	strategy_expenditures	salesVisit_expenditures	percentage_profit	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	Hospital	4	107352.00	7205615.00	28640790.00	795320.00	4875000.00	104625000.00	...	1575000.00	-265616725.00	265616725.00	36641725.00	228975000.00	-100.00	2.71	10.78	0.30	86.21
1	Hospital	5	4510.00	0.00	989550.00	0.00	0.00	5475000.00	...	0.00	-12014550.00	12014550.00	989550.00	11025000.00	-100.00	0.00	8.24	0.00	91.76
2	Pharmacy	4	56849.00	3184058.00	6928169.00	0.00	225000.00	23625000.00	...	150000.00	-79937227.00	79937227.00	10112227.00	69825000.00	-100.00	3.98	8.67	0.00	87.35
3	Pharmacy	5	1780.00	0.00	0.00	0.00	0.00	3075000.00	...	0.00	-4125000.00	4125000.00	0.00	4125000.00	-100.00	0.00	0.00	0.00	100.00
4	Polyclinic	4	54980.00	1406941.00	11045266.00	0.00	1275000.00	84750000.00	...	75000.00	-145052207.00	145052207.00	12452207.00	132600000.00	-100.00	0.97	7.61	0.00	91.42
5	Polyclinic	5	4420.00	501649.00	390754.00	0.00	75000.00	12075000.00	...	75000.00	-16867403.00	16867403.00	892403.00	15975000.00	-100.00	2.97	2.32	0.00	94.71
6	Private Clinic	4	6704.00	186625.00	2872348.00	82439.00	0.00	9825000.00	...	75000.00	-21666412.00	21666412.00	3141412.00	18525000.00	-100.00	0.86	13.26	0.38	85.50
7	Private Clinic	5	1118.00	54000.00	0.00	0.00	0.00	3525000.00	...	0.00	-5154000.00	5154000.00	54000.00	5100000.00	-100.00	1.05	0.00	0.00	98.95

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	Hospital	4	229371468	229371468.00	105994.21	105994.21	NaN	NaN	NaN	NaN
1	Hospital	5	14490207	14490207.00	105001.50	105001.50	NaN	NaN	NaN	NaN
2	Pharmacy	4	52889259	52889259.00	107063.28	107063.28	NaN	NaN	NaN	NaN
3	Pharmacy	5	11933112	11933112.00	104676.42	104676.42	NaN	NaN	NaN	NaN
4	Polyclinic	4	36651699	36651699.00	105019.19	105019.19	NaN	NaN	NaN	NaN
5	Polyclinic	5	31111328	31111328.00	104751.95	104751.95	NaN	NaN	NaN	NaN
6	Private Clinic	4	5114191	5114191.00	213091.29	213091.29	NaN	NaN	NaN	NaN
7	Private Clinic	5	6818921	6818921.00	104906.48	104906.48	NaN	NaN	NaN	NaN

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_salesVisit
0	Hospital	4	1891994983	1386419983.00	104455.09	76542.81	27912.27	100.00
1	Hospital	5	686878479	584578479.00	104372.96	88828.21	15544.75	100.00
2	Pharmacy	4	850404740	617604740.00	107892.00	78356.35	29535.65	100.00
3	Pharmacy	5	281706693	240381693.00	104879.63	89494.30	15385.33	100.00
4	Polyclinic	4	115495475	-30229525.00	105959.15	-27733.51	133692.66	100.00
5	Polyclinic	5	92907799	58557799.00	104743.85	66017.81	38726.04	100.00
6	Private Clinic	4	161523199	127848199.00	104953.35	83072.25	21881.09	100.00
7	Private Clinic	5	18752032	11477032.00	105348.49	64477.71	40870.79	100.00

Loading the data and simple check¶

Return orders, no order and successful orders¶

Return Orders¶

Succesful Orders¶

No Orders¶

Checking the distribution and density with accType¶

Analysing the combination of strategies¶

Time Series Analysis for New Drug¶

Analysing the performance of best strategies quarterly for each category¶

Statistical testing¶

Finding:¶

4. Best Result from Tukey Test¶

a. Over all:¶

b. presence of new drug¶

	accID	accType	accSize	accTargets	district	month	sales	qty	salesVisit2	salesVisit3	salesVisit5	compBrand
1	XYZ-987002	Pharmacy	700	25	3	2013-10-01	0	0	0.00	0.00	0.00	4
2	XYZ-987002	Pharmacy	700	25	3	2013-11-01	852365	8	0.00	0.00	75000.00	4
3	XYZ-987002	Pharmacy	700	25	3	2013-12-01	2557096	25	0.00	0.00	0.00	4
4	XYZ-987002	Pharmacy	700	25	3	2014-01-01	4261826	41	825000.00	300000.00	0.00	4
5	XYZ-987002	Pharmacy	700	25	3	2014-02-01	5966556	57	0.00	0.00	0.00	4

	accID	accType	accSize	accTargets	district	month	sales	qty	strategy1	...	salesVisit3	salesVisit4	compBrand	total_expenditures	strategy_expenditures	salesVisit_expenditures	net_income	net_income_per_unit	unit_price
622	XYZ-987036	Hospital	130	8	3	2015-07-01	-4261826	-16	0.00	...	0.00	0.00	5	375000.00	0.00	375000.00	-4636826.00	-289801.62	-266364.12
623	XYZ-987036	Hospital	130	8	3	2015-08-01	-5540374	-21	54000.00	...	0.00	0.00	5	429000.00	54000.00	375000.00	-5969374.00	-284255.90	-263827.33
633	XYZ-987037	Hospital	300	14	3	2014-06-01	-1491639	-3	97979.00	...	375000.00	75000.00	4	1222979.00	97979.00	1125000.00	-2714618.00	-904872.67	-497213.00
705	XYZ-987045	Hospital	370	15	3	2014-06-01	-852365	-8	0.00	...	450000.00	0.00	4	750000.00	0.00	750000.00	-1602365.00	-200295.62	-106545.62
709	XYZ-987045	Hospital	370	15	3	2014-10-01	-852365	-8	0.00	...	600000.00	0.00	4	900000.00	0.00	900000.00	-1752365.00	-219045.62	-106545.62
1264	XYZ-987083	Polyclinic	200	4	3	2015-01-01	-213091	-1	0.00	...	0.00	0.00	4	300000.00	0.00	300000.00	-513091.00	-513091.00	-213091.00
1530	XYZ-987096	Hospital	280	11	3	2015-03-01	-2130913	-8	0.00	...	1425000.00	0.00	4	1425000.00	0.00	1425000.00	-3555913.00	-444489.12	-266364.12
1534	XYZ-987096	Hospital	280	11	3	2015-07-01	-1278548	-5	0.00	...	1050000.00	0.00	5	1050000.00	0.00	1050000.00	-2328548.00	-465709.60	-255709.60
2329	XYZ-987150	Hospital	240	9	3	2013-10-01	-852365	-8	0.00	...	0.00	0.00	4	0.00	0.00	0.00	-852365.00	-106545.62	-106545.62
2336	XYZ-987150	Hospital	240	9	3	2014-05-01	-596656	-6	0.00	...	150000.00	0.00	4	450000.00	0.00	450000.00	-1046656.00	-174442.67	-99442.67
2818	XYZ-987181	Hospital	160	4	3	2014-07-01	-852365	-8	0.00	...	450000.00	0.00	4	450000.00	0.00	450000.00	-1302365.00	-162795.62	-106545.62

	accType	compBrand	accSize	sales	qty	strategy1	salesVisit2	...	net_income	total_expenditures	strategy_expenditures	salesVisit_expenditures	percentage_profit	percentage_strategy1	percentage_salesVisit	percentage_loss
0	Hospital	4	1960.00	-7628668.00	-49.00	97979.00	1575000.00	...	-12826647.00	5197979.00	97979.00	5100000.00	-246.76	1.88	98.12	246.76
1	Hospital	5	540.00	-11080748.00	-42.00	54000.00	750000.00	...	-12934748.00	1854000.00	54000.00	1800000.00	-697.67	2.91	97.09	697.67
2	Polyclinic	4	200.00	-213091.00	-1.00	0.00	300000.00	...	-513091.00	300000.00	0.00	300000.00	-171.03	0.00	100.00	171.03

	compBrand	placed_qty	sales	net_income
0	4	85298	8970802557	6728082722.00
1	5	35279	3699818908	3065700542.00

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy1
0	Hospital	4	11080747	10698031.00	135131.06	130463.79	4667.27	100.00
1	Pharmacy	4	852365	57109.00	106545.62	7138.62	99407.00	100.00
2	Polyclinic	4	0	-183750.00	NaN	-inf	NaN	100.00
3	Polyclinic	5	0	-408440.00	NaN	-inf	NaN	100.00
4	Private Clinic	4	0	-104233.00	NaN	-inf	NaN	100.00

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy1	percentage_salesVisit
0	Hospital	4	559279413	426113148.00	105944.20	80718.54	25225.66	18.79	81.21
1	Hospital	5	90776892	76592434.00	100195.25	84539.11	15656.13	15.93	84.07
2	Pharmacy	4	297560682	190662277.00	105705.39	67730.83	37974.57	9.84	90.16
3	Pharmacy	5	28724707	25621328.00	103699.30	92495.77	11203.53	13.00	87.00
4	Polyclinic	4	7671287	4395670.00	103666.04	59400.95	44265.09	33.60	66.40
5	Polyclinic	5	0	-318209.00	NaN	-inf	NaN	29.29	70.71
6	Private Clinic	4	3409461	3024438.00	103317.00	91649.64	11667.36	41.56	58.44
7	Private Clinic	5	0	-354000.00	NaN	-inf	NaN	15.25	84.75

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy2
0	Hospital	4	14490208	11715263.00	103501.49	83680.45	19821.04	100.00
1	Hospital	5	33242242	31965710.00	103882.01	99892.84	3989.16	100.00
2	Pharmacy	4	8097469	7214695.00	122688.92	109313.56	13375.36	100.00
3	Polyclinic	4	0	-774674.00	NaN	-inf	NaN	100.00
4	Polyclinic	5	2557095	1641345.00	106545.62	68389.38	38156.25	100.00
5	Private Clinic	4	0	-223000.00	NaN	-inf	NaN	100.00

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy2	percentage_salesVisit
0	Hospital	4	1667439384	1140275808.00	104456.52	71432.43	33024.09	29.31	70.69
1	Hospital	5	932913688	763087276.00	106278.62	86931.79	19346.82	30.44	69.56
2	Pharmacy	4	1132409760	730664895.00	104872.18	67666.69	37205.49	18.72	81.28
3	Pharmacy	5	898137193	720747071.00	103878.93	83361.91	20517.02	19.16	80.84
4	Polyclinic	4	27701869	-16930424.00	104142.36	-63648.21	167790.58	26.06	73.94
5	Polyclinic	5	27275684	18604227.00	104504.54	71280.56	33223.97	37.73	62.27
6	Private Clinic	4	98874360	70130786.00	104078.27	73821.88	30256.39	35.03	64.97
7	Private Clinic	5	32389877	23872319.00	104147.51	76759.87	27387.65	42.77	57.23

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy3	percentage_salesVisit
0	Hospital	4	33242242	14079667.00	103882.01	43998.96	59883.05	40.51	59.49
1	Hospital	5	28128051	21882103.00	103793.55	80745.77	23047.78	14.74	85.26
2	Pharmacy	4	27275686	17359446.00	104105.67	66257.43	37848.24	33.44	66.56
3	Private Clinic	5	5114191	3432576.00	104371.24	70052.57	34318.67	15.26	84.74

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy1	percentage_strategy2	percentage_salesVisit
0	Hospital	4	527017394	355085745.00	103580.46	69788.86	33791.60	8.85	23.49	67.66
1	Hospital	5	76712865	61903197.00	107440.99	86699.16	20741.83	13.00	27.24	59.76
2	Pharmacy	4	1145152623	784158654.00	104905.88	71835.71	33070.17	9.01	16.18	74.81
3	Pharmacy	5	346912630	284144473.00	103928.29	85124.17	18804.12	8.88	20.14	70.98
4	Polyclinic	4	2557096	-2669733.00	102283.84	-106789.32	209073.16	10.59	23.41	66.01

	compBrand	return_qty	sales_loss	net_income_loss
0	4	-50	-7841759	-13339738.00
1	5	-42	-11080748	-12934748.00

	compBrand	placed_qty	sales	net_income	return_qty	sales_loss	net_income_loss	precentage_return	precentage_sale_loss	precentage_net_income_loss_on_return
0	4	85298	8970802557	6728082722.00	-50	-7841759	-13339738.00	0.06	0.09	0.20
1	5	35279	3699818908	3065700542.00	-42	-11080748	-12934748.00	0.12	0.30	0.42

	compBrand	net_income_loss_zero
0	4	-512272571.00
1	5	-38160953.00

	compBrand	placed_qty	return_qty	precentage_return	precentage_sale_loss	precentage_net_income_loss_on_return	precentage_net_income_loss
0	4	85298	-50	0.06	0.09	0.20	7.81
1	5	35279	-42	0.12	0.30	0.42	1.67

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	Hospital	4	14064026	5307846.00	114341.67	43153.22	71188.46	21.33	26.42	52.25
1	Hospital	5	26764266	12739816.00	104141.11	49571.27	54569.84	19.42	22.29	58.29
2	Pharmacy	4	39208799	26624689.00	104002.12	70622.52	33379.60	3.15	12.22	84.63
3	Private Clinic	4	0	-1077971.00	NaN	-inf	NaN	1.90	7.65	90.45

	accType	compBrand	sales	net_income	unit_price	net_income_per_unit	expenditures_per_unit	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	Hospital	4	0	-1994540.00	NaN	-inf	NaN	0.71	27.43	19.21	52.64
1	Pharmacy	5	14490208	10103044.00	104246.10	72683.77	31562.33	32.79	10.56	20.76	35.90

	type_of_agg	accType	compBrand	qty	sales	net_income	strategy1	strategy2	strategy3	total_expenditures	strategy_expenditures	salesVisit_expenditures	unit_price	net_income_per_unit	expenditures_per_unit	percentage_profit	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	data_with_zero_expenditures	Hospital	4	2164	229371468	229371468.00	0.00	0.00	0.00	0.00	0.00	0.00	105994.21	105994.21	0.00	0.00	NaN	NaN	NaN	NaN
1	data_only_with_salesVisit_expenditures	Hospital	4	18113	1891994983	1386419983.00	0.00	0.00	0.00	505575000.00	0.00	505575000.00	104455.09	76542.81	27912.27	274.23	0.00	0.00	0.00	100.00
2	data_only_with_strategy1_expenditures	Hospital	4	82	11080747	10698031.00	382716.00	0.00	0.00	382716.00	382716.00	0.00	135131.06	130463.79	4667.27	2795.29	100.00	0.00	0.00	0.00
3	data_with_strategy1_and_salesVisit_expenditures	Hospital	4	5279	559279413	426113148.00	25016265.00	0.00	0.00	133166265.00	25016265.00	108150000.00	105944.20	80718.54	25225.66	319.99	18.79	0.00	0.00	81.21
4	data_only_with_strategy2_expenditures	Hospital	4	140	14490208	11715263.00	0.00	2774945.00	0.00	2774945.00	2774945.00	0.00	103501.49	83680.45	19821.04	422.18	0.00	100.00	0.00	0.00
5	data_with_strategy2_and_salesVisit_expenditures	Hospital	4	15963	1667439384	1140275808.00	0.00	154488576.00	0.00	527163576.00	154488576.00	372675000.00	104456.52	71432.43	33024.09	216.30	0.00	29.31	0.00	70.69
6	data_with_strategy3_and_salesVisit_expenditures	Hospital	4	320	33242242	14079667.00	0.00	0.00	7762575.00	19162575.00	7762575.00	11400000.00	103882.01	43998.96	59883.05	73.47	0.00	0.00	40.51	59.49
7	data_only_with_strategy1_and_strategy2_expenditures	Hospital	4	16	1704730	439374.00	441232.00	824124.00	0.00	1265356.00	1265356.00	0.00	106545.62	27460.88	79084.75	34.72	34.87	65.13	0.00	0.00
8	data_with_strategy1_strategy2_and_salesVisit_expenditures	Hospital	4	5088	527017394	355085745.00	15224039.00	40382610.00	0.00	171931649.00	55606649.00	116325000.00	103580.46	69788.86	33791.60	206.53	8.85	23.49	0.00	67.66
9	data_with_strategy2_strategy3_and_salesVisit_expenditures	Hospital	4	123	14064026	5307846.00	0.00	1867506.00	2313674.00	8756180.00	4181180.00	4575000.00	114341.67	43153.22	71188.46	60.62	0.00	21.33	26.42	52.25
10	data_with_all_type_of_expenditures	Hospital	4	0	0	-1994540.00	14220.00	547200.00	383120.00	1994540.00	944540.00	1050000.00	NaN	-inf	NaN	-100.00	0.71	27.43	19.21	52.64
11	data_with_zero_expenditures	Hospital	5	138	14490207	14490207.00	0.00	0.00	0.00	0.00	0.00	0.00	105001.50	105001.50	0.00	0.00	NaN	NaN	NaN	NaN
12	data_only_with_salesVisit_expenditures	Hospital	5	6581	686878479	584578479.00	0.00	0.00	0.00	102300000.00	0.00	102300000.00	104372.96	88828.21	15544.75	571.44	0.00	0.00	0.00	100.00
13	data_with_strategy1_and_salesVisit_expenditures	Hospital	5	906	90776892	76592434.00	2259458.00	0.00	0.00	14184458.00	2259458.00	11925000.00	100195.25	84539.11	15656.13	539.97	15.93	0.00	0.00	84.07
14	data_only_with_strategy2_expenditures	Hospital	5	320	33242242	31965710.00	0.00	1276532.00	0.00	1276532.00	1276532.00	0.00	103882.01	99892.84	3989.16	2504.11	0.00	100.00	0.00	0.00
15	data_with_strategy2_and_salesVisit_expenditures	Hospital	5	8778	932913688	763087276.00	0.00	51701412.00	0.00	169826412.00	51701412.00	118125000.00	106278.62	86931.79	19346.82	449.33	0.00	30.44	0.00	69.56
16	data_with_strategy3_and_salesVisit_expenditures	Hospital	5	271	28128051	21882103.00	0.00	0.00	920948.00	6245948.00	920948.00	5325000.00	103793.55	80745.77	23047.78	350.34	0.00	0.00	14.74	85.26
17	data_with_strategy1_strategy2_and_salesVisit_expenditures	Hospital	5	714	76712865	61903197.00	1924930.00	4034738.00	0.00	14809668.00	5959668.00	8850000.00	107440.99	86699.16	20741.83	417.99	13.00	27.24	0.00	59.76
18	data_with_strategy2_strategy3_and_salesVisit_expenditures	Hospital	5	257	26764266	12739816.00	0.00	2724056.00	3125394.00	14024450.00	5849450.00	8175000.00	104141.11	49571.27	54569.84	90.84	0.00	19.42	22.29	58.29
19	data_with_zero_expenditures	Pharmacy	4	494	52889259	52889259.00	0.00	0.00	0.00	0.00	0.00	0.00	107063.28	107063.28	0.00	0.00	NaN	NaN	NaN	NaN
20	data_only_with_salesVisit_expenditures	Pharmacy	4	7882	850404740	617604740.00	0.00	0.00	0.00	232800000.00	0.00	232800000.00	107892.00	78356.35	29535.65	265.29	0.00	0.00	0.00	100.00
21	data_only_with_strategy1_expenditures	Pharmacy	4	8	852365	57109.00	795256.00	0.00	0.00	795256.00	795256.00	0.00	106545.62	7138.62	99407.00	7.18	100.00	0.00	0.00	0.00
22	data_with_strategy1_and_salesVisit_expenditures	Pharmacy	4	2815	297560682	190662277.00	10523405.00	0.00	0.00	106898405.00	10523405.00	96375000.00	105705.39	67730.83	37974.57	178.36	9.84	0.00	0.00	90.16
23	data_only_with_strategy2_expenditures	Pharmacy	4	66	8097469	7214695.00	0.00	882774.00	0.00	882774.00	882774.00	0.00	122688.92	109313.56	13375.36	817.28	0.00	100.00	0.00	0.00
24	data_with_strategy2_and_salesVisit_expenditures	Pharmacy	4	10798	1132409760	730664895.00	0.00	75194865.00	0.00	401744865.00	75194865.00	326550000.00	104872.18	67666.69	37205.49	181.87	0.00	18.72	0.00	81.28
25	data_with_strategy3_and_salesVisit_expenditures	Pharmacy	4	262	27275686	17359446.00	0.00	0.00	3316240.00	9916240.00	3316240.00	6600000.00	104105.67	66257.43	37848.24	175.06	0.00	0.00	33.44	66.56
26	data_with_strategy1_strategy2_and_salesVisit_expenditures	Pharmacy	4	10916	1145152623	784158654.00	32509642.00	58409327.00	0.00	360993969.00	90918969.00	270075000.00	104905.88	71835.71	33070.17	217.22	9.01	16.18	0.00	74.81
27	data_with_strategy2_strategy3_and_salesVisit_expenditures	Pharmacy	4	377	39208799	26624689.00	0.00	396950.00	1537160.00	12584110.00	1934110.00	10650000.00	104002.12	70622.52	33379.60	211.57	0.00	3.15	12.22	84.63
28	data_with_zero_expenditures	Pharmacy	5	114	11933112	11933112.00	0.00	0.00	0.00	0.00	0.00	0.00	104676.42	104676.42	0.00	0.00	NaN	NaN	NaN	NaN
29	data_only_with_salesVisit_expenditures	Pharmacy	5	2686	281706693	240381693.00	0.00	0.00	0.00	41325000.00	0.00	41325000.00	104879.63	89494.30	15385.33	581.69	0.00	0.00	0.00	100.00
30	data_with_strategy1_and_salesVisit_expenditures	Pharmacy	5	277	28724707	25621328.00	403379.00	0.00	0.00	3103379.00	403379.00	2700000.00	103699.30	92495.77	11203.53	825.59	13.00	0.00	0.00	87.00
31	data_with_strategy2_and_salesVisit_expenditures	Pharmacy	5	8646	898137193	720747071.00	0.00	33990122.00	0.00	177390122.00	33990122.00	143400000.00	103878.93	83361.91	20517.02	406.31	0.00	19.16	0.00	80.84
32	data_with_strategy1_strategy2_and_salesVisit_expenditures	Pharmacy	5	3338	346912630	284144473.00	5575765.00	12642392.00	0.00	62768157.00	18218157.00	44550000.00	103928.29	85124.17	18804.12	452.69	8.88	20.14	0.00	70.98
33	data_with_all_type_of_expenditures	Pharmacy	5	139	14490208	10103044.00	1438464.00	463116.00	910584.00	4387164.00	2812164.00	1575000.00	104246.10	72683.77	31562.33	230.29	32.79	10.56	20.76	35.90
34	data_with_zero_expenditures	Polyclinic	4	349	36651699	36651699.00	0.00	0.00	0.00	0.00	0.00	0.00	105019.19	105019.19	0.00	0.00	NaN	NaN	NaN	NaN
35	data_only_with_salesVisit_expenditures	Polyclinic	4	1090	115495475	-30229525.00	0.00	0.00	0.00	145725000.00	0.00	145725000.00	105959.15	-27733.51	133692.66	-20.74	0.00	0.00	0.00	100.00
36	data_only_with_strategy1_expenditures	Polyclinic	4	0	0	-183750.00	183750.00	0.00	0.00	183750.00	183750.00	0.00	NaN	-inf	NaN	-100.00	100.00	0.00	0.00	0.00
37	data_with_strategy1_and_salesVisit_expenditures	Polyclinic	4	74	7671287	4395670.00	1100617.00	0.00	0.00	3275617.00	1100617.00	2175000.00	103666.04	59400.95	44265.09	134.19	33.60	0.00	0.00	66.40
38	data_only_with_strategy2_expenditures	Polyclinic	4	0	0	-774674.00	0.00	774674.00	0.00	774674.00	774674.00	0.00	NaN	-inf	NaN	-100.00	0.00	100.00	0.00	0.00
39	data_with_strategy2_and_salesVisit_expenditures	Polyclinic	4	266	27701869	-16930424.00	0.00	11632293.00	0.00	44632293.00	11632293.00	33000000.00	104142.36	-63648.21	167790.58	-37.93	0.00	26.06	0.00	73.94
40	data_with_strategy1_strategy2_and_salesVisit_expenditures	Polyclinic	4	25	2557096	-2669733.00	553479.00	1223350.00	0.00	5226829.00	1776829.00	3450000.00	102283.84	-106789.32	209073.16	-51.08	10.59	23.41	0.00	66.01
41	data_with_zero_expenditures	Polyclinic	5	297	31111328	31111328.00	0.00	0.00	0.00	0.00	0.00	0.00	104751.95	104751.95	0.00	0.00	NaN	NaN	NaN	NaN
42	data_only_with_salesVisit_expenditures	Polyclinic	5	887	92907799	58557799.00	0.00	0.00	0.00	34350000.00	0.00	34350000.00	104743.85	66017.81	38726.04	170.47	0.00	0.00	0.00	100.00
43	data_only_with_strategy1_expenditures	Polyclinic	5	0	0	-408440.00	408440.00	0.00	0.00	408440.00	408440.00	0.00	NaN	-inf	NaN	-100.00	100.00	0.00	0.00	0.00
44	data_with_strategy1_and_salesVisit_expenditures	Polyclinic	5	0	0	-318209.00	93209.00	0.00	0.00	318209.00	93209.00	225000.00	NaN	-inf	NaN	-100.00	29.29	0.00	0.00	70.71
45	data_only_with_strategy2_expenditures	Polyclinic	5	24	2557095	1641345.00	0.00	915750.00	0.00	915750.00	915750.00	0.00	106545.62	68389.38	38156.25	179.24	0.00	100.00	0.00	0.00
46	data_with_strategy2_and_salesVisit_expenditures	Polyclinic	5	261	27275684	18604227.00	0.00	3271457.00	0.00	8671457.00	3271457.00	5400000.00	104504.54	71280.56	33223.97	214.55	0.00	37.73	0.00	62.27
47	data_with_zero_expenditures	Private Clinic	4	24	5114191	5114191.00	0.00	0.00	0.00	0.00	0.00	0.00	213091.29	213091.29	0.00	0.00	NaN	NaN	NaN	NaN
48	data_only_with_salesVisit_expenditures	Private Clinic	4	1539	161523199	127848199.00	0.00	0.00	0.00	33675000.00	0.00	33675000.00	104953.35	83072.25	21881.09	379.65	0.00	0.00	0.00	100.00
49	data_only_with_strategy1_expenditures	Private Clinic	4	0	0	-104233.00	104233.00	0.00	0.00	104233.00	104233.00	0.00	NaN	-inf	NaN	-100.00	100.00	0.00	0.00	0.00
50	data_with_strategy1_and_salesVisit_expenditures	Private Clinic	4	33	3409461	3024438.00	160023.00	0.00	0.00	385023.00	160023.00	225000.00	103317.00	91649.64	11667.36	785.52	41.56	0.00	0.00	58.44
51	data_only_with_strategy2_expenditures	Private Clinic	4	0	0	-223000.00	0.00	223000.00	0.00	223000.00	223000.00	0.00	NaN	-inf	NaN	-100.00	0.00	100.00	0.00	0.00
52	data_with_strategy2_and_salesVisit_expenditures	Private Clinic	4	950	98874360	70130786.00	0.00	10068574.00	0.00	28743574.00	10068574.00	18675000.00	104078.27	73821.88	30256.39	243.99	0.00	35.03	0.00	64.97
53	data_with_strategy2_strategy3_and_salesVisit_expenditures	Private Clinic	4	0	0	-1077971.00	0.00	20532.00	82439.00	1077971.00	102971.00	975000.00	NaN	-inf	NaN	-100.00	0.00	1.90	7.65	90.45
54	data_with_zero_expenditures	Private Clinic	5	65	6818921	6818921.00	0.00	0.00	0.00	0.00	0.00	0.00	104906.48	104906.48	0.00	0.00	NaN	NaN	NaN	NaN
55	data_only_with_salesVisit_expenditures	Private Clinic	5	178	18752032	11477032.00	0.00	0.00	0.00	7275000.00	0.00	7275000.00	105348.49	64477.71	40870.79	157.76	0.00	0.00	0.00	100.00
56	data_with_strategy1_and_salesVisit_expenditures	Private Clinic	5	0	0	-354000.00	54000.00	0.00	0.00	354000.00	54000.00	300000.00	NaN	-inf	NaN	-100.00	15.25	0.00	0.00	84.75
57	data_with_strategy2_and_salesVisit_expenditures	Private Clinic	5	311	32389877	23872319.00	0.00	3642558.00	0.00	8517558.00	3642558.00	4875000.00	104147.51	76759.87	27387.65	280.27	0.00	42.77	0.00	57.23
58	data_with_strategy3_and_salesVisit_expenditures	Private Clinic	5	49	5114191	3432576.00	0.00	0.00	256615.00	1681615.00	256615.00	1425000.00	104371.24	70052.57	34318.67	204.12	0.00	0.00	15.26	84.74

	accID	accType	accSize	accTargets	district	month	sales	qty	strategy1	strategy2	...	salesVisit4	salesVisit5	compBrand	total_expenditures	strategy_expenditures	salesVisit_expenditures	net_income	net_income_per_unit	unit_price	quarter
21	XYZ-987002	Pharmacy	700	25	3	2015-06-01	11933113	115	0.00	115380.00	...	0.00	0.00	5	2440380.00	115380.00	2325000.00	9492733.00	82545.50	103766.20	2015Q2
22	XYZ-987002	Pharmacy	700	25	3	2015-07-01	12785478	123	0.00	416114.00	...	0.00	0.00	5	3341114.00	416114.00	2925000.00	9444364.00	76783.45	103946.98	2015Q3

	accID	month	forcasted_sales	forcasted_sales_lower	forcasted_sales_upper
20	XYZ-987002	2015-06-01	8314022.05	5363377.39	11160660.77
21	XYZ-987002	2015-07-01	8411392.36	5544101.50	11328531.31

	accType	sales	forcasted_sales	precent_sales_dff	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	Hospital	1889906690	2166787371.84	14.65	1.30	18.51	1.25	78.94
1	Pharmacy	1581904543	1738509123.66	9.90	2.57	16.30	0.32	80.82
2	Polyclinic	153851906	98577950.57	-35.93	1.12	9.37	0.00	89.50
3	Private Clinic	63075021	91801836.35	45.54	0.30	20.43	1.44	77.83

	type_of_agg	accType	quarter	compBrand	qty	sales	net_income	strategy1	strategy2	strategy3	...	strategy_expenditures	salesVisit_expenditures	unit_price	net_income_per_unit	expenditures_per_unit	percentage_profit	percentage_strategy1	percentage_strategy2	percentage_strategy3	percentage_salesVisit
0	data_with_zero_expenditures	Hospital	2013Q4	4	1566	165870262	165870262.00	0.00	0.00	0.00	...	0.00	0.00	105919.71	105919.71	0.00	0.00	NaN	NaN	NaN	NaN
1	data_with_zero_expenditures	Hospital	2014Q1	4	140	14490209	14490209.00	0.00	0.00	0.00	...	0.00	0.00	103501.49	103501.49	0.00	0.00	NaN	NaN	NaN	NaN